{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d809f1cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as tck\n",
    "import seaborn as sns\n",
    "import re\n",
    "import json\n",
    "from tqdm.notebook import tqdm, trange\n",
    "from tqdm import tqdm\n",
    "from scipy import stats\n",
    "import scipy.stats as st\n",
    "from math import ceil\n",
    "from collections import Counter\n",
    "\n",
    "from pymongo import InsertOne, DeleteMany, ReplaceOne, UpdateOne\n",
    "\n",
    "import query_builder\n",
    "import grocery_base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c7c3c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "font_size =12\n",
    "\n",
    "SMALL_SIZE = font_size\n",
    "MEDIUM_SIZE = font_size\n",
    "BIGGER_SIZE = font_size\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)  # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)  # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)  # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)  # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)  # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)  # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title\n",
    "\n",
    "matplotlib.rcParams['font.serif'] = \"Times New Roman\"\n",
    "matplotlib.rcParams['font.family'] = \"serif\"\n",
    "sns.set_style({'font.family': 'serif', 'font.serif': 'Times New Roman'})\n",
    "# sns.set_style(\"white\")\n",
    "# sns.set_style(\"ticks\")\n",
    "sns.set(style=\"ticks\", font=\"Times New Roman\", font_scale=1.5)\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.max_rows', 200)\n",
    "\n",
    "pd.set_option('display.max_colwidth', 200) # so long string like whoolefoods originalID will fit!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d6b6fc0",
   "metadata": {},
   "source": [
    "## Load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7c34278c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50468\n"
     ]
    }
   ],
   "source": [
    "gdb_df=pd.read_csv('GroceryDB_foods.csv')\n",
    "print(len(gdb_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "284c1f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open and load the JSON file\n",
    "with open('UpdatedProductIngredients_11_15.json', 'r') as file:\n",
    "    ing_list=json.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d8c41581",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to flatten json\n",
    "def flatten_ingredients(product_name,original_id,ingredients,parent_order=0,depth=1):\n",
    "    rows=[]\n",
    "\n",
    "    for ingredient in ingredients:\n",
    "        # Calculate distance to first node\n",
    "        distance_to_root=ingredient.get('order',0)+parent_order\n",
    "\n",
    "        # Extract ingredient information, calculated distance, depth, and parent_order\n",
    "        row = {\n",
    "            'product_name': product_name,\n",
    "            'original_ID': original_id,\n",
    "            'ingredient_name': ingredient.get('ingredient_name'),\n",
    "            'original_text': ingredient.get('original_text'),\n",
    "            'general_name': ingredient.get('general_name'),\n",
    "            'ingredient_type': ingredient.get('ingredient_type'),\n",
    "            'order': ingredient.get('order'),\n",
    "            'descriptors': ingredient.get('descriptors', []),\n",
    "            'parent_order': parent_order,\n",
    "            'distance_to_root': distance_to_root,\n",
    "            'depth': depth\n",
    "        }\n",
    "        rows.append(row)\n",
    "\n",
    "        # Rerun function for sub-ingredients\n",
    "        if ingredient.get('sub_ingredients'):\n",
    "            rows.extend(flatten_ingredients(product_name,original_id,\n",
    "                ingredient['sub_ingredients'],parent_order=distance_to_root,depth=depth+1\n",
    "            ))\n",
    "\n",
    "    return rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5f6408e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "610765"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Convert json into Dataframe\n",
    "all_rows=[]\n",
    "for product in ing_list:\n",
    "    product_name=product.get('product_name')\n",
    "    original_id=product.get('original_ID') \n",
    "    if product_name and product.get('ingredient_tree'):  \n",
    "        all_rows.extend(flatten_ingredients(product_name,original_id,product['ingredient_tree']))\n",
    "\n",
    "p_ingred=pd.DataFrame(all_rows)\n",
    "len(p_ingred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf44cadd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 42540/42540 [14:07<00:00, 50.17it/s]\n"
     ]
    }
   ],
   "source": [
    "#Calculate the width and depth of each product ingredient tree\n",
    "p_ingred['tree_width']=None\n",
    "p_ingred['sum_tree_depth']=None\n",
    "p_ingred['ingred_count']=None\n",
    "\n",
    "prods=p_ingred['original_ID'].unique()\n",
    "\n",
    "for prod_id in tqdm(prods):\n",
    "    S=p_ingred[p_ingred['original_ID']==prod_id]\n",
    "    tree_width=S[S['parent_order']==0]['order'].max()\n",
    "    sum_tree_depth=len(S[['parent_order','depth']].drop_duplicates())\n",
    "    p_ingred.loc[S.index,'tree_width']=tree_width\n",
    "    p_ingred.loc[S.index,'ingred_count']=len(S)\n",
    "    p_ingred.loc[S.index,'sum_tree_depth']=sum_tree_depth\n",
    "    #if len(S)<=1:\n",
    "    #    p_ingred.loc[S.index,'sum_tree_depth']=0\n",
    "    #else:\n",
    "    #    p_ingred.loc[S.index,'sum_tree_depth']=sum_tree_depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4487a8cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine information from gdb_df into p_ingred\n",
    "merged_df = p_ingred.merge(\n",
    "    gdb_df[['original_ID','store','harmonized single category','f_FPro']],\n",
    "    on='original_ID', how='left')\n",
    "p_ingred['store'] = merged_df['store']\n",
    "p_ingred['harmonized single category'] = merged_df['harmonized single category']\n",
    "p_ingred['f_FPro'] = merged_df['f_FPro']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f8c3799b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19602"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p_ingred['original_text'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e9d3db4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14363"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(p_ingred['general_name'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f5a691",
   "metadata": {},
   "source": [
    "### Cochran's formula functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c87226f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cochran's formula for determining sample size\n",
    "def get_sample_size(N, cl, e, p):\n",
    "    \n",
    "    # Get z-score\n",
    "    z = st.norm.ppf(1 - (1 - cl) / 2)\n",
    "    # Get n_0 value\n",
    "    n_0 = z**2 * p * (1 - p) / e**2\n",
    "    # Calculate n\n",
    "    n = n_0 / (1 + (n_0 - 1) / N)\n",
    "    # Round up to the nearest integer\n",
    "    n = ceil(n)\n",
    "    return n\n",
    "\n",
    "#https://towardsdatascience.com/how-to-sample-data-with-code-327359dce10b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e2302d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_true_count_sample_csv(column_name, pop_df, filename_out, count_df, sample_size):\n",
    "\n",
    "    count_adds = count_df.copy()\n",
    "\n",
    "    count_adds = pop_df[\n",
    "        pop_df[column_name].isin(count_adds[column_name])]\\\n",
    "    [[column_name, 'ingredient_type']].drop_duplicates()\n",
    "    \n",
    "    #Create binary column, 1 = ingredient and 0 = additive\n",
    "    count_adds['true_ingredient']=0\n",
    "    S=count_adds[count_adds['ingredient_type']=='ingredient'] \n",
    "    count_adds.loc[S.index, 'true_ingredient']=1\n",
    "    \n",
    "    count_adds = pd.merge(count_adds, count_df, on=column_name)\n",
    "        \n",
    "    count_adds.sample(sample_size).to_csv(filename_out)\n",
    "    \n",
    "    print(\"generated file 'ingredients_with_desc_sample.csv' with a sample size of: \",sample_size)\n",
    "    \n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7e741a6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_true_count_stats(filename, column_name, count_df):\n",
    "\n",
    "    annotated_df = pd.read_csv(filename)\n",
    "\n",
    "    percent_add = ((annotated_df['ingredient_type']=='additive')&\\\n",
    "                   (annotated_df['true_ingredient']==0)).sum()/len(annotated_df)\n",
    "    \n",
    "    estimated_num_add = round(percent_add*len(count_df))\n",
    "\n",
    "    percent_good = annotated_df['true_ingredient'].sum()/len(annotated_df)\n",
    "    estimated_num_ig = round(percent_good*len(count_df))\n",
    "    error = (len(count_df)*0.05)\n",
    "    \n",
    "    print('for ' + column_name+\":\")\n",
    "    print()\n",
    "    print(\"percent good additives:\", percent_add)\n",
    "    print(\"estimated total unique additives:\", estimated_num_add)\n",
    "    print(error)\n",
    "    print()\n",
    "    print(\"percent good ingreds:\", percent_good)\n",
    "    print(\"estimated total unique ingredients:\", estimated_num_ig)\n",
    "    print(error)\n",
    "    \n",
    "    return percent_good, percent_add"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57fd0f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_confidence_interval(estimated_proportion, sample_size, confidence_level, count_df_size):\n",
    "    z = st.norm.ppf(1 - (1-confidence_level)/2)\n",
    "    lower_bound = estimated_proportion - z*np.sqrt((estimated_proportion*(1-estimated_proportion))/sample_size)\n",
    "    upper_bound = estimated_proportion + z*np.sqrt((estimated_proportion*(1-estimated_proportion))/sample_size)\n",
    "    return lower_bound*count_df_size, upper_bound*count_df_size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c5a154",
   "metadata": {},
   "source": [
    "# Rules for Ingredients (including additives)\n",
    "\n",
    "1. Ingredient definition: a component of a food. Products like \"tacos\", sub recipes like \"filling\" and blends of ingredients are not counted as ingredients.\n",
    "2. Only 1 ingredient in a group with the same general_name will be randomly chosen to be the \"true ingredient\" \n",
    "3. two ingredients have been placed under the same \"general_name\" if one is a plural of the other, or one or both have a descriptor which is not directly relevant to the contents of the ingredient. ex. walmart beans will have  beans as a general name but kidney beans will be assumed different to pinto beans. \n",
    "4. if there are typos or extra information which does not refer to the type of ingredient they are not counted as ingredients. We want to see how many typos we have in the data dist and they are likely represented properly elsewhere.\n",
    "5. accuracy is subject to the annotaters knowledge of spellings and ingredients, google searches were used in most cases, but not all.\n",
    "\n",
    "\n",
    "# Identifying Additives\n",
    "1. matched using additive dictionary\n",
    "2. powders, flavors, and extracts are assumed additives for this analysis\n",
    "3. common household spices not considered additives (basil, for example)\n",
    "\n",
    "# Counting Unique Descriptors\n",
    "1. descriptors should be a process, extra information resulting from a process (like puree, for example), and or a label providing information about growing or collection (organic)\n",
    "2. descriptors which are synonyms of the related ingredient are not counted (vitamin b \\<niacin\\>)\n",
    "3. descriptors which are synonyms are only counted once\n",
    "4. only descriptors on a string marked \"true ingredient\" are counted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ce2fc6",
   "metadata": {},
   "source": [
    "# All strings with descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d75a572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of ingredients: 19602\n",
      "generated file 'ingredients_with_desc_sample.csv' with a sample size of:  377\n"
     ]
    }
   ],
   "source": [
    "#Find the number of ingredients in dataset\n",
    "column_name = 'ingredient_name'\n",
    "count_df = p_ingred[column_name].value_counts()\n",
    "count_df = pd.DataFrame(count_df).reset_index()\n",
    "count_df.columns = [column_name, 'count']\n",
    "print('total number of ingredients:',len(count_df))\n",
    "\n",
    "#Cochran's formula for determining sample size\n",
    "pop_size = len(count_df)\n",
    "confidence = 0.95  # desired confidence\n",
    "error = 0.05 # desired margin of error\n",
    "p = 0.5 # target proportion, 0.5 is most conservative\n",
    "\n",
    "Cochran_sample_size = get_sample_size(pop_size, confidence, error, p)\n",
    "\n",
    "#randomly sample dataset to Cochran_sample_size\n",
    "sample_size = generate_true_count_sample_csv(column_name,p_ingred,\n",
    "    'ingredients_with_desc_sample.csv',count_df=count_df,sample_size=Cochran_sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7071640d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for ingredient_name:\n",
      "\n",
      "percent good additives: 0.07427055702917772\n",
      "estimated total unique additives: 1456\n",
      "980.1\n",
      "\n",
      "percent good ingreds: 0.9257294429708223\n",
      "estimated total unique ingredients: 18146\n",
      "980.1\n",
      "ingredient counts: 17627.31483812673 18664.982244101386\n",
      "additive counts: 937.0177558986148 1974.6851618732683\n"
     ]
    }
   ],
   "source": [
    "confidence = 0.95  # desired confidence\n",
    "sample_size = Cochran_sample_size\n",
    "\n",
    "estimated_prop, additive_prop = generate_true_count_stats('ingredients_with_desc_sample.csv',\n",
    "                                                        'ingredient_name',count_df)\n",
    "\n",
    "ing_lower_desc, ing_upper_desc = calculate_confidence_interval(estimated_prop,\n",
    "                                                               sample_size,\n",
    "                                                               confidence,\n",
    "                                                               len(count_df))\n",
    "\n",
    "add_lower_desc, add_upper_desc = calculate_confidence_interval(additive_prop,\n",
    "                                                               sample_size,\n",
    "                                                               confidence,\n",
    "                                                               len(count_df))\n",
    "\n",
    "print('ingredient counts:',ing_lower_desc, ing_upper_desc)\n",
    "print('additive counts:', add_lower_desc, add_upper_desc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e71b70",
   "metadata": {},
   "source": [
    "# Strings occurring>1 with descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23171883",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of ingredients: 10006\n",
      "generated file 'ingredients_with_desc_sample.csv' with a sample size of:  370\n"
     ]
    }
   ],
   "source": [
    "column_name = 'ingredient_name'\n",
    "count_df = p_ingred[column_name].value_counts()\n",
    "count_df = pd.DataFrame(count_df).reset_index()\n",
    "count_df.columns = [column_name, 'count']\n",
    "count_df = count_df[count_df['count']>1]\n",
    "print('total number of ingredients:',len(count_df))\n",
    "\n",
    "pop_size = len(count_df)\n",
    "confidence = 0.95  # desired confidence\n",
    "error = 0.05 # desired margin of error\n",
    "p = 0.5 # target proportion, 0.5 is most conservative\n",
    "\n",
    "Cochran_sample_size = get_sample_size(pop_size, confidence, error, p)\n",
    "\n",
    "#randomly sample dataset to Cochran_sample_size\n",
    "sample_size = generate_true_count_sample_csv(column_name,p_ingred,\n",
    "    'filtered_ingredients_with_desc_sample.csv',count_df=count_df,sample_size=Cochran_sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0f65192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for ingredient_name with >1 descriptor:\n",
      "\n",
      "percent good additives: 0.11081081081081082\n",
      "estimated total unique additives: 1109\n",
      "500.3\n",
      "\n",
      "percent good ingreds: 0.8891891891891892\n",
      "estimated total unique ingredients: 8897\n",
      "500.3\n",
      "ingredient counts: 8577.193203040917 9217.260851013138\n",
      "additive counts: 788.7391489868635 1428.8067969590825\n"
     ]
    }
   ],
   "source": [
    "confidence = 0.95  # desired confidence\n",
    "sample_size = Cochran_sample_size\n",
    "\n",
    "estimated_prop, additive_prop = generate_true_count_stats('filtered_ingredients_with_desc_sample.csv',\n",
    "                                                        'ingredient_name with >1 descriptor',count_df)\n",
    "\n",
    "ing_lower_desc_filt, ing_upper_desc_filt = calculate_confidence_interval(estimated_prop,\n",
    "                                                                         sample_size,\n",
    "                                                                         confidence,\n",
    "                                                                         len(count_df))\n",
    "\n",
    "add_lower_desc_filt, add_upper_desc_filt = calculate_confidence_interval(additive_prop,\n",
    "                                                                         sample_size,\n",
    "                                                                         confidence,\n",
    "                                                                         len(count_df))\n",
    "\n",
    "print('ingredient counts:',ing_lower_desc_filt, ing_upper_desc_filt)\n",
    "print('additive counts:', add_lower_desc_filt, add_upper_desc_filt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d3b4f2",
   "metadata": {},
   "source": [
    "# All unfiltered strings without descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6c70cd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of ingredients: 14363\n",
      "generated file 'ingredients_with_desc_sample.csv' with a sample size of:  375\n"
     ]
    }
   ],
   "source": [
    "column_name = 'general_name'\n",
    "count_df = p_ingred[column_name].value_counts()\n",
    "count_df = pd.DataFrame(count_df).reset_index()\n",
    "count_df.columns = [column_name, 'count']\n",
    "print('total number of ingredients:',len(count_df))\n",
    "\n",
    "pop_size = len(count_df)\n",
    "confidence = 0.95  # desired confidence\n",
    "error = 0.05 # desired margin of error\n",
    "p = 0.5 # target proportion, 0.5 is most conservative\n",
    "\n",
    "Cochran_sample_size = get_sample_size(pop_size, confidence, error, p)\n",
    "\n",
    "#randomly sample dataset to Cochran_sample_size\n",
    "sample_size = generate_true_count_sample_csv(column_name,p_ingred,\n",
    "    'general_names_sample.csv',count_df=count_df,sample_size=Cochran_sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ea91f3d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for general name:\n",
      "\n",
      "percent good additives: 0.058666666666666666\n",
      "estimated total unique additives: 843\n",
      "718.1500000000001\n",
      "\n",
      "percent good ingreds: 0.9413333333333334\n",
      "estimated total unique ingredients: 13520\n",
      "718.1500000000001\n",
      "ingredient counts: 13178.749286433322 13861.992046900014\n",
      "additive counts: 501.00795309998784 1184.2507135666788\n"
     ]
    }
   ],
   "source": [
    "confidence = 0.95  # desired confidence\n",
    "sample_size = Cochran_sample_size\n",
    "\n",
    "estimated_prop, additive_prop = generate_true_count_stats('general_names_sample.csv',\n",
    "                                                       'general name',count_df)\n",
    "\n",
    "ing_lower, ing_upper = calculate_confidence_interval(estimated_prop,\n",
    "                                                                    sample_size,\n",
    "                                                                    confidence,\n",
    "                                                                    len(count_df))\n",
    "\n",
    "add_lower, add_upper = calculate_confidence_interval(additive_prop,\n",
    "                                                                    sample_size,\n",
    "                                                                    confidence,\n",
    "                                                                    len(count_df))\n",
    "\n",
    "print('ingredient counts:',ing_lower, ing_upper)\n",
    "print('additive counts:', add_lower, add_upper)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f096ff7",
   "metadata": {},
   "source": [
    "We expect this number to be less in reality as only synonyms which were within the sample were removed from the count. synonyms across the entire corpus are likely much more prevelant than what we calculated, and thus this is an approximate upper bound. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ab0f8f",
   "metadata": {},
   "source": [
    "# All strings which occur more than once without descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca0206bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total number of ingredients: 7407\n",
      "generated file 'ingredients_with_desc_sample.csv' with a sample size of:  366\n"
     ]
    }
   ],
   "source": [
    "column_name = 'general_name'\n",
    "count_df = p_ingred[column_name].value_counts()\n",
    "count_df = pd.DataFrame(count_df).reset_index()\n",
    "count_df.columns = [column_name, 'count']\n",
    "count_df = count_df[count_df['count']>1]\n",
    "print('total number of ingredients:',len(count_df))\n",
    "\n",
    "pop_size = len(count_df)\n",
    "confidence = 0.95  # desired confidence\n",
    "error = 0.05 # desired margin of error\n",
    "p = 0.5 # target proportion, 0.5 is most conservative\n",
    "\n",
    "Cochran_sample_size = get_sample_size(pop_size, confidence, error, p)\n",
    "\n",
    "#randomly sample dataset to Cochran_sample_size\n",
    "sample_size = generate_true_count_sample_csv(column_name,p_ingred,\n",
    "    'general_name_morethan1_sample.csv',count_df=count_df,sample_size=Cochran_sample_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c5a8c483",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for cleaned name:\n",
      "\n",
      "percent good additives: 0.08743169398907104\n",
      "estimated total unique additives: 648\n",
      "370.35\n",
      "\n",
      "percent good ingreds: 0.912568306010929\n",
      "estimated total unique ingredients: 6759\n",
      "370.35\n",
      "ingredient counts: 6545.046629856345 6973.740255389557\n",
      "additive counts: 433.25974461044336 861.953370143655\n"
     ]
    }
   ],
   "source": [
    "confidence = 0.95  # desired confidence\n",
    "sample_size = Cochran_sample_size\n",
    "\n",
    "estimated_prop, additive_prop = generate_true_count_stats('general_name_morethan1_sample.csv',\n",
    "                                                        'cleaned name',count_df)\n",
    "\n",
    "ing_lower_filt, ing_upper_filt = calculate_confidence_interval(estimated_prop,\n",
    "                                                                         sample_size,\n",
    "                                                                         confidence,\n",
    "                                                                         len(count_df))\n",
    "\n",
    "add_lower_filt, add_upper_filt = calculate_confidence_interval(additive_prop,\n",
    "                                                                         sample_size,\n",
    "                                                                         confidence,\n",
    "                                                                         len(count_df))\n",
    "\n",
    "print('ingredient counts:',ing_lower_filt, ing_upper_filt)\n",
    "print('additive counts:', add_lower_filt, add_upper_filt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d5e515",
   "metadata": {},
   "source": [
    "We expect these numbers to be larger in reality given that not all of the single occurence items are issues."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dc833ad",
   "metadata": {},
   "source": [
    "We expect this number to be less in reality as only synonyms which were within the sample were removed from the count. synonyms across the entire corpus are likely much more prevelant than what we calculated, and thus this is an approximate upper bound. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9914a4",
   "metadata": {},
   "source": [
    "# Count Descriptors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db145496",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 610765/610765 [00:00<00:00, 3784022.56it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>descriptor</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>modified</td>\n",
       "      <td>5311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>whole</td>\n",
       "      <td>5115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>pasteurized</td>\n",
       "      <td>4719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>enriched</td>\n",
       "      <td>4339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dried</td>\n",
       "      <td>4240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>groundwhite</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>dextrose-vegetable</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>cloud</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>dehydra-ted</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1772</th>\n",
       "      <td>tangy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1773 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              descriptor  count\n",
       "0               modified   5311\n",
       "1                  whole   5115\n",
       "2            pasteurized   4719\n",
       "3               enriched   4339\n",
       "4                  dried   4240\n",
       "...                  ...    ...\n",
       "1768         groundwhite      1\n",
       "1769  dextrose-vegetable      1\n",
       "1770               cloud      1\n",
       "1771         dehydra-ted      1\n",
       "1772               tangy      1\n",
       "\n",
       "[1773 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "# count all DESCRIPTORS in two ways\n",
    "desc_count_ALL = Counter()\n",
    "for x in tqdm(p_ingred['descriptors']):\n",
    "    for y in x:\n",
    "        desc_count_ALL[y]+=1\n",
    "        \n",
    "        \n",
    "# convert to df for easy export       \n",
    "desc_count_all_df = pd.DataFrame.from_dict(desc_count_ALL, orient='index').reset_index()\n",
    "desc_count_all_df.columns = ['descriptor','count']\n",
    "desc_count_all_df.sort_values('count',inplace=True,ascending=False)\n",
    "desc_count_all_df.reset_index().drop(labels=['index'],axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ff4fc79a",
   "metadata": {},
   "outputs": [],
   "source": [
    "desc_count_all_df_filt = desc_count_all_df[desc_count_all_df['count']>10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf25975b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "373"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(desc_count_all_df_filt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d839a0",
   "metadata": {},
   "source": [
    "# Preparing plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4ae7fce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with descriptors\n",
    "ing_desc_error = (ing_upper_desc-ing_lower_desc)/2\n",
    "ing_desc = ing_lower_desc + ing_desc_error\n",
    "\n",
    "# with descriptors, additives\n",
    "add_desc_error = (add_upper_desc-add_lower_desc)/2\n",
    "add_desc = add_lower_desc + add_desc_error\n",
    "\n",
    "# with descriptors, filtered\n",
    "ing_desc_filt_error = (ing_upper_desc_filt-ing_lower_desc_filt)/2\n",
    "ing_desc_filt = ing_lower_desc_filt + ing_desc_filt_error\n",
    "\n",
    "# with descriptors, filtered, additives\n",
    "ing_filt_error = (ing_upper_filt-ing_lower_filt)/2\n",
    "ing_filt = ing_lower_filt + ing_filt_error\n",
    "\n",
    "# without descriptors\n",
    "ing_error = (ing_upper-ing_lower)/2\n",
    "ing = ing_lower + ing_error\n",
    "\n",
    "# without descriptors, additives\n",
    "add_error = (add_upper-add_lower)/2\n",
    "add = add_lower + add_error\n",
    "\n",
    "# without descriptors, filtered\n",
    "ing_filt_error = (ing_upper_filt-ing_lower_filt)/2\n",
    "ing_filt = ing_lower_filt + ing_filt_error\n",
    "\n",
    "# without descriptors, filtered, additives\n",
    "add_filt_error = (add_upper_filt-add_lower_filt)/2\n",
    "add_filt = add_lower_filt + add_filt_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "75c727a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=['ingredients (including adds) with descriptors',\n",
    "                    'additives with descriptors',\n",
    "                    'filtered ingredients (including adds) with descriptors',\n",
    "                    'filtered additives with descriptors',\n",
    "                    'ingredients (including adds) without descriptors',\n",
    "                    'additives without descriptors',\n",
    "                    'filtered ingredients (including adds) without descriptors',\n",
    "                    'filtered additives without descriptors']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aa8ed816",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=[ing_desc,\n",
    "    add_desc,\n",
    "    ing_desc_filt, \n",
    "    ing_filt,\n",
    "    ing,\n",
    "    add,\n",
    "    ing_filt,\n",
    "    add_filt\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7e846c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_series=[\n",
    "    ing_desc_error,\n",
    "    add_desc_error,\n",
    "    ing_desc_filt_error,\n",
    "    ing_filt_error,\n",
    "    ing_error,\n",
    "    add_error,\n",
    "    ing_filt_error,\n",
    "    add_filt_error\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f76ad90f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_df = pd.DataFrame(y,x).reset_index()\n",
    "plot_df.columns = ['counts', 'type']\n",
    "plot_df['counts'] = plot_df['counts'].apply(round)\n",
    "plot_df['error'] = error_series\n",
    "plot_df['error'] = plot_df['error']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a276398f",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_filter = plot_df[~plot_df['type'].str.contains('filtered')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a006be1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>counts</th>\n",
       "      <th>type</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18146</td>\n",
       "      <td>ingredients (including adds) with descriptors</td>\n",
       "      <td>518.833703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1456</td>\n",
       "      <td>additives with descriptors</td>\n",
       "      <td>518.833703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13520</td>\n",
       "      <td>ingredients (including adds) without descriptors</td>\n",
       "      <td>341.621380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>843</td>\n",
       "      <td>additives without descriptors</td>\n",
       "      <td>341.621380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   counts                                              type       error\n",
       "0   18146     ingredients (including adds) with descriptors  518.833703\n",
       "1    1456                        additives with descriptors  518.833703\n",
       "4   13520  ingredients (including adds) without descriptors  341.621380\n",
       "5     843                     additives without descriptors  341.621380"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0f6cef8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_filter_fixed_labels = no_filter.copy()\n",
    "no_filter_fixed_labels['type'] = no_filter['type']\\\n",
    ".str.replace('ingredients \\(including adds\\)',repl='')\\\n",
    ".str.replace('additives',repl='')\\\n",
    ".str.replace('without descriptors', repl='without incorporating\\ndescriptors')\\\n",
    ".str.replace('with descriptors', repl='incorporating\\ndescriptors')\\\n",
    ".str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e9f10e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_filter_fixed_labels = no_filter_fixed_labels.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "48f86352",
   "metadata": {},
   "outputs": [],
   "source": [
    "ing_df = no_filter_fixed_labels.iloc[[0,2]]\n",
    "add_df = no_filter_fixed_labels.iloc[[1,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7557d943",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# from this paper https://www.sciencedirect.com/science/article/pii/S0889157521001204\n",
    "ingID = [[8,6500,'Ahuja et al.',0]]\n",
    "#ing_df = ing_df.append(pd.DataFrame(ingID,columns=ing_df.columns), ignore_index=False)\n",
    "ing_df = pd.concat([ing_df, pd.DataFrame(ingID, columns=ing_df.columns)], ignore_index=False)\n",
    "ing_df=ing_df.reset_index()\n",
    "ing_df = ing_df.drop(['index','level_0'],axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2084a256",
   "metadata": {},
   "source": [
    "### Figure S12 part 1-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ae959709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2)\n",
    "\n",
    "bar1 = sns.barplot(ax=axs[0],x=ing_df['counts'],\n",
    "            y=ing_df['type'])\n",
    "\n",
    "axs[0].errorbar(y=ing_df['type'],\n",
    "            x=ing_df['counts'],\n",
    "             xerr=ing_df['error'],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "# Define some hatches\n",
    "hatches = ['', '', '//']\n",
    "\n",
    "# Loop over the bars\n",
    "for i,thisbar in enumerate(bar1.patches):\n",
    "    # Set a different hatch for each bar\n",
    "    thisbar.set_hatch(hatches[i])\n",
    "\n",
    "axs[0].set_xlabel(xlabel='')\n",
    "axs[0].set_xlim([0,20000])\n",
    "\n",
    "axs[0].set_title(\"Estimated Numbers of Unique Ingredients\")\n",
    "\n",
    "sns.barplot(ax=axs[1],x=add_df['counts'],\n",
    "            y=add_df['type'])\n",
    "\n",
    "axs[1].errorbar(y=add_df['type'],\n",
    "            x=add_df['counts'],\n",
    "             xerr=add_df['error'],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "axs[1].set_xlim([0,20000])\n",
    "\n",
    "axs[1].set_title(\"Estimated Numbers of Unique Additives\")\n",
    "\n",
    "\n",
    "axs[0].bar_label(axs[0].containers[0], label_type='center')\n",
    "axs[1].bar_label(axs[1].containers[0], label_type='center', padding=-7)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4)\n",
    "\n",
    "#plt.savefig('estimated_numbers_unique_ingredients_additives_horizontal.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fa2325df",
   "metadata": {},
   "outputs": [],
   "source": [
    "ing_df = ing_df[~ing_df['type'].str.contains('Ahuja')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5368b6d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '18146'), Text(0, 0, '13520')]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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e/zKZTJgxY4bSBHR5Oed5oereduzYMeG3336TPnv27Jnw+++/C+bm5oKpqak0PXvixvT0dGHo0KFCw4YNpev/n3/+kZYZMWKENO+ePXvkroXLly8LLVu2lOqomIzzyZMnwq+//irdM6Ojo4UhQ4YIRkZGwh9//KGwT9OnTxeaNm0qbT8iIkLo1auXwrF///69MHXqVKFp06ZKj7EgCMLatWuFnj17CmFhYYIgZN7rmzZtKtSrV0+4evXqFx0rLy8vldsdNWqU4OLiIiQnJ0vbbd68uTBv3jxVp1KO+N3wzz//KHyW0/fKqlWrpOPRpk0b4dmzZ4K5ublgZWUl1KtXTzAyMhKaNm2qkPD13bt3QocOHQQ3NzchJSVFEARB2Lx5s2BkZCR06tRJSExMlJu/TZs2CtdOfHy8YGtrq/R75c6dO0qvEUHIvH7q168vLF26VEhNTRXS0tKEDRs2SPNnPb6bNm2Svouyruvhw4fC4MGDpeleXl7CzJkzhcaNGwuWlpbS9OzfX4IgSNvatm2bIAiZiZXFbZibm0vHIz86deok1a9//vlH2n5uyaY/5/u0II6fSNXvgoULF0plyJ7Y+eLFi1LdUnZ809LSBDMzM8HPz09uP4cMGSLMmDFDSEpKEmQymbBnzx7ByMhIaNmypRARESHN+znfaQMGDBCMjIyEU6dOqZwnO7aYICKir87JyQm2trbSPxMTE/Tt21dpIq+zZ88iNDQU3bt3B5DZTN3R0RFdu3bN07Zq1qyJZcuWoXPnzgqfdenSBZs2bZL+XrVqFdzc3HDlyhXcuHEDJiYmkMlkWLFiBY4dO4ZTp07h4sWLOH/+PCpWrIiYmBhcvnxZWv7KlSt48eIFmjZtKiW9srCwQJMmTRAfH4979+7lqczR0dGYNGkSJk2aJL2RqFixotSiZN26dXJvYlauXIkrV65g8eLFMDIyAgBUr14dc+fOzdP2crJgwQIpIeKGDRtw5syZXJepVasWypUrp/Sz7G/YdHV1MXbsWEyePBkA8P79e3h5eeHIkSO4ePEiTpw4gdKlS+Pjx4+YNGkStLW1cePGDVy+fBm7du2ChoYGrl+/rjQhWWRkJF68eIFr167h/PnzOH78OKpXrw6ZTIbZs2fLJVmdMmUKLC0t0bNnT2hoaKBYsWJwc3ND6dKlcePGDfj4+KBChQrYtm0bhg8fDiAz6Z6xsTEuX74MJycnTJw4ETo6Oti9ezdMTU1Ro0YNAJndk5YsWYKSJUvm4YhnDiU3Z84cDBgwAK1btwaQmcdgxowZaNasGW7evImlS5fmaV1fQ8+ePbFjxw7p7yVLlmDx4sW4evUqrl27hv/9739IT0+XG+O+R48e2LlzJ4oWLap0ne7u7vDy8sKWLVvwv//9D0BmPpMpU6bIDdcoUjW8sIaGBho1aqT0s7yc8y/Rvn17eHp6Sv3Sly9fjjFjxuDGjRu4efOmVObsyXp9fHxw+fJldO7cWbr+GzZsKLUyunr1qtR6qW/fvjh+/LjUj37fvn04duwY3N3d0bFjR3Tt2hVpaWmYMGECHB0d0a5dOwBA6dKlsWjRImhqasLb2xs3b96Utr9v3z4cOnQIM2bMkLZfvnx5LF68WKGbULly5bBw4UKppVN2N2/exKZNm7BixQpUrFgRAGBmZobhw4cjLS0Ns2fPRkZGxmcfK1XCwsJw9uxZdO7cWTo2ZmZmmDp1ap6WDwkJkVrxVK9eXeHznL5XnJ2dpdZ6nz59woYNG7Br1y5cunQJFy5cQNmyZREbGyv33SEIAsaPHw9tbW3MmDEDurq6ACB9z/n7++PKlSu5lrt48eIqh57Mfu8VvX//HhMmTICpqSlcXV2ho6MDbW1tODs7K00oOnz4cKU5EIyNjbF9+3ZpOx4eHvjll19w8+ZNXLt2DaNHjwYAhVZcQUFBWL16NapXr47BgwcDyEyyKp6r6OhoPHr0KNd9z+ratWvQ0NCQWrs0bNhQKtfu3btzXDa/36cFdfxyM3XqVJWtfVq3bi3VxazDfouCgoJQrVo1udYXGzZswPv37zF37lwULVoUGhoa6Nu3L1q3bo3379/LDQP9Od9pVapUAQCVLRSVYWCCiIi+um3btuHkyZPSvzt37uDPP/9UOm9UVBQAKDR57tGjR762qSw7dpEiRaRkjwDg6uoqNUXX19eHo6MjgMzmtStWrEDlypUBZAYJxJEqQkNDpeUrVaoEHR0dhUza4g/yvDZp9PLywqdPn9C2bVu56bVr1waQOfqIGCCIiIjAzp07YWRkpNCs0tzcPM8JDVUpWrQo1q9fj/Lly0MQBEyZMgWvXr3KdTlVyU6VTc96HvT19TFnzhwpA36NGjWkh6mqVavC1dUV+vr6ADL375dffgGQ2R88u9KlS8PNzU1K1FizZk0sWbIEQOaPt/PnzwPIDDDcuXNHoStNkSJFpIeSrF0Rfv75Z6ms/fr1g76+PqZMmSIFLKKionDt2jW8efNGWqZEiRJ5HrFh7dq1SEtLQ4cOHRQ+Gzp0KIDMH/0fPnzI0/oKm66urtyIBTNnzpS7jsTm7FmvFXE5ZT9s4+LisHbtWrRr1066dkQaGhrSD96sckquqywZan7P+efQ1dWFvr6+NErK2LFjYW1tDQ0NDWhra0sPndmbTV+8eBEApIcikVjWtLQ06b4IZNYtcRu9e/dGqVKlYGFhgRUrVqB69eo4e/YsgoKCFPa1bNmy0gg24r4mJydj1apVKF26NDp16iQ3f9WqVaUgUXaqRh/Yvn076tevL907RWJ3grdv3+LJkyeffaxUiYyMBAAcOHBArvtJx44dpftHTp4/fw4gsz7k9PClbL+zXg+amppYuHChdC7LlSsnBRuzXg8XL17Ew4cP4eDgIFeXy5UrB2dnZ1hZWaFhw4a5llvcpjKqkgKvX78ecXFx6NOnj8Jn2b+DRMpGPhKDKeK+29jYYPDgwVJgSPzOzn4OL1++jIyMDPzvf/+TC3xZWFhI9SEiIkJpOVTZtWuXFOQQDRw4EEBmYCQ6Olrpcp/zfVpQxy8vVF1nAKTA5YkTJ5Camir32YEDB9C3b1/p79TUVOzevRtt27ZVqBfitXnmzBmpm9fnfKeJ17zYtSovvu9Uq0RE9EPQ1tZGnz595N4giczNzQEAy5Ytw8OHDzFhwgTUqlULTZo0yfc2lBF/TAGKoxKUL18eQObDVfY3hQYGBgAg1xe9Xr16uH//vvTWLyIiAl5eXtLbSFV9ubO7efMmBEFAly5dFD4Tf6iJP7x9fX2Rlpam9EerhoYGSpUq9cWJKytUqIB169ahX79+0vB5hw4dynMLgLwQz4OyhwbxPCgbCUI8D8nJyQqfKfsh3rBhQ5iamuLu3bt4+vQpOnfuLJ2fWbNmKdSThIQElC5dWkosmXW9NWvWVLov5ubm2L17N7p164bhw4ejf//+UvAiN4mJiVLARNnwhJaWltDV1UVqaiquXbuW55ZDOfmcZJnZl8marb5MmTJyn4nnT1neBmVZ7i9evIhPnz6hXr16SrddEKOu5PecfwlxH8W6KhKPS/a6q6oPftb6kP0YiPugrE6K+yoGzrKSyWQoXbq09Jb1woULiImJgYWFhdJ7ZtahaJVtP6uMjAzcuXMHmpqaCg8w6enp0rqytnbK77FSpVatWihbtiwuXLiAbt26wdXVFVZWVtDV1cWECRNyXV4MdOY24pCq7xVxerFixRQSZ4qtybJeD5cuXQIAVKtWTWFdLi4uuZb3c2VkZEitUJR9h5QqVUrpcjmNTiHue/ZzKO53Xus7kPlwGxMTk6/RLQIDA/H06VOFvB8dOnRA5cqVERoaCk9PT6XXQ36/Twvj+OUkp+NQt25dmJiY4P79+/Dz85N+P6SkpODixYtwdXWV5n327Bni4uJw6NAhuZZsQOb5Ea/NmJgYlClT5rO+08TvcvG3Sp72L89zEhERFbJatWopTKtXrx7+/PNPLFiwAGfOnMG5c+dga2uLKVOmfPawonmV01tY8TMhW0JIHR0dBAQESF1EevTogeDgYBw+fDjP2w0LC0OpUqUUfjAoIzZxzf4jsKA1bNgQCxYswMSJExEUFISJEydi8+bNhbpN0eech5z88ssvuHv3rpT5XHyDt2nTJpXBhvyYMGEC3r59iwsXLmDFihVwd3eHs7Mz+vfvn+sP7ODgYOltl7KAgY6ODqpWrYpXr14ptED4XPk9hunp6QoPxjkFN8R587p+cZSFvLzZ/lwFfc5zourYqAqwNG7cGBcuXFA4v+Lxq1ixosoAgTLivvr4+KjsOiMqyPtJbGwsEhMT8dtvv2HlypV5Wia/x0qVokWLYvXq1ZgwYQKeP3+OYcOGoWHDhlKi2tyID5+5HS9VcroexHtA1uvh3bt3APIevC4ob968kfa1oL5DVO27qntf48aNASi2qAL+PUZia8G82LVrF9LS0mBvb6/wmRgU2b9/P5ycnBTqVX7rf2Ecvy/Rt29f3L9/H/v27ZMCE35+fmjbtq3csMbisR41apTSlh7Zfc53mnjt5Ccwwa4cRET0zZg0aZLS6X369MHx48dhZ2cHILOpop2dXZ5zNnxN27dvx7Bhw9C/f38sXrxYaR/T3KSnpyMqKipPb2zFeb50fPa86NSpE5ydnQFk5tMQ+1F/b8RmtGILjIyMDACQa6b6JYoXL46NGzdi7dq1qFWrFmJiYqR++MryqGSV9W2iqubLYkuVL+2mIxLfCicmJuZp/sTExEINGogBo6w5QApaQZ/zgtSnTx9Ur14dp0+flruuX7x4AQBSvp28Evc1MDAw13kL8n4iriMv2y0Mpqam8PPzg7OzM/T09PDw4UP06dMn1xwDwL/XREGNzpIbMSARHBz8VbYnyjraR04tFwpTkyZNYGtri2fPnsldj4mJiQgODkazZs3kulzmJD4+HsePH8eJEyfkuouK/44dOwYdHR2EhoZKLdOyym/9/xaOX1Y2NjYoW7YsHjx4IHWhOHDgAHr37i03X37uCcDnfaeJAe/8tA5hYIKIiL5JAQEBcn9XrVoVy5Ytg7e3N+rVq4eEhASlw/+pk6enJxYtWoSZM2eqTECWF2I+Bz8/P6WfC4IgDZUoNhUtqLfnuZkwYQLat28PIHOYVrEc3xPxx2TdunUB/NvEWNXxBjKHCM2v9u3bw9fXF25ubtDT08OdO3dyHXJOzF8BqH5oFn9UZs9B8LnE/c9rP+7w8HCl3UwKihhwEYeCLAyFdc4LQsmSJbF//35kZGRgyZIlSElJwYcPH7B48WI0aNBAaRP0nIj7euLECZXziPsq3k/EN/hfonTp0tDR0cHz58/x+vVrpfNERESo/KwgFC9eHC4uLjhz5gzs7OwgCAIWLlyocnhakdiXP69dR76UmEtFVX2UyWRyw3/mJD9ds7K2vPla3yHKLF26FFZWVpg1axaioqKQkpKChQsXQkdHJ19JnD09PWFlZaXQnUxkaGgo5SwSh1fOKr/fp9/K8RPp6upKuTz27t2LFy9eQE9PT6GLkHhPOHPmjMogzL179xRyVeTnO00MdKvqzqIMAxNERPTNuXz5stQaYvv27XIPaHXq1MGePXtQuXJlBAUF5auZYGHbv38/ACgkehNlb6ar6gekmHRrxYoVCAkJUfj88OHDUr9ssRns7du3c3xjk5+uDjnR0NDA4sWLpQSfqn6Mic04s75RAv59G67Ot0tPnjxBiRIlpCR04vE+fvw4zp49qzD/69evce7cuTyvf9asWdL/a2pqolevXlKG8/v37+e4rKGhoTSKRPbs9aKwsDCUK1dOoTXO557jRo0aQVNTEzExMbm+QXv69Ck0NDRUJkEsCGLdOnv2rNKkpmJgJjuxzmXPSq+szhX0OS9oK1euhK6uLl6+fImOHTtiyJAhsLCwwM6dO+WaZOeFuK87duzAw4cPFT6/deuWNF0cueDFixdyCTazy0td09XVRaNGjSAIAmbMmKH0IX/lypVf1PpG1T300aNHOHDggPR32bJlsWzZMnTo0AHp6elKj0NWYuAtMTEx11ZOBaFZs2YAMhMFivkmsvLw8JDLh5QTsX5kv/dmzY0gPnDWqFFDeri+fv26ynUW1PeHKn5+frh37x5KlSqFHj16wN7eHqmpqTh06JCU4Dg3GRkZ2LNnj5S0WpVevXoByPzOFFshifL7fVoYx+9zcv5k1bt3b2hra+P48ePYuHGj0uNRv359FC1aFKGhoUpHeEpKSsLWrVulOvc532nifTc/XeUYmCAioq8i68NETv1o4+PjsWDBAumthiAI2LZtm9w8enp6qF+/PvT09OQSIooPJsqa34o/MrK/Hcj64yP7Z2I5lT0IiT80si4jTnN3d0dGRgbS0tLg5eUlJfWMjo5GUFCQ9CZY/AGZvby9evWCnp4eoqOj0atXL+zfvx8hISF48+YNtmzZgk2bNsHa2hpA5pCnJUqUQHR0tMLIJVnltUmy+JYjp/n19PSwfv36HDOEi8GZAwcOICMjAzKZDL6+vlLujZCQEKSlpSkcR2Vvb/J7HrJ/ltXDhw+lJKpil4jmzZujTp06EAQBEyZMwPLly+Hv7493797h2LFjGDZsGBwcHBTKo+qB5cGDB7h7967cNPHBQ9lQl9mNGTMGAHDu3DmFN7vPnz/H+/fvMW7cOIX+0eK5y28XiCpVqqBbt24AoHCtZfXp0yfMmTMHo0ePVth21vqi6g2csh/64gNS1mXatWuHUqVKITExEcOGDYO/v780j7u7u0JrKpFY5/bt2ydtb9euXfDy8gIA6c28IAj5Puc5ybrvyh6+xc9VBVSyH5cTJ07gwIEDmDVrFjZt2oTTp0/jyJEjcHFxUfkQL9ZJZdvv1KkTypUrh5SUFAwcOBBbtmxBYGAgQkJCcODAAUybNk1Kotq6dWtUqVIF6enp2LBhQ572Oes+ZN8XcRjRe/fuoXfv3jh//jwiIiLw9OlTTJs2DZ8+fZK7JvJ7rFTdQwFgz549CtPzeh3Wr19f+m7JqfWIqu8Vcbs5dQnI+pmtra1UfydPnoxjx44hOTkZHz58wPr167Fr1y6FUXpUbVtcj6+vr9Q14Z9//pFrYfj69WsIggBNTU3pwXXHjh0qEyVnr1dZ36RnPye5ncPsy4SGhmL69Ono27cvli5dilOnTuHEiRNYuHChXAuy3IhJKHPLIWJubi4FE7LnScrv92lhHL+c6rSq6yyrihUrwtraGikpKbh37540glhW+vr6UssKd3d3jBs3Dvfu3UNERARu3LiBwYMHS0OtAp/3nSa+VMlP61EGJoiI6KvI+mZdVT/a4OBgDB8+HJUqVZJLJOXp6YnFixdLX/r+/v64desWRo4cKfcWSUyeee/ePSQlJUk/rGUyGf7++28AwN9//y33g0mcDkBhrPTbt29L5cr69jA9PV16S3D//n1pfS1btgSQmWTO0tISzZs3x61bt6SuD2vXrsXkyZOlcopvncXWITt37sTHjx9RsWJFzJ8/H9ra2oiMjMScOXPQrl072NraYvXq1Zg/f770gGJgYICFCxdCW1sbS5cuxaFDhyCTyZCeno7NmzdLb5z9/Pzw4MEDpcc9q9OnTwPIzM6fk8qVK2Pt2rUq3+LZ2NgAyAxMWFpawsLCAt7e3hg3bhwA4OPHj+jatavUz1c8D8HBwXJv+jIyMqQfRI8ePZI7d1FRUdJDatbzqKenhzJlyiA0NBRz586V1vfy5UtMmDABI0aMQL9+/aT5NTQ0sGzZMhgaGiItLQ2bNm2CnZ0drK2t4erqCkdHR6nbhyAIuHPnDoDM+qGsSbggCBg7dizOnz8vBUf27t2LEiVKKAxhp4yVlRXGjBmD1NRUjBkzRvqB9/btW0yZMgXdu3dXGC43Pj4e165dAwBcu3Yt3yOxTJs2DZaWljh48CD+/PNPuZYKSUlJ8PX1hYODA0xMTOSOnShrvpfsb9CePn0KIPNtcNYf6AEBAfj48SOAf681IPNHs5ubGzQ1NfHy5UvY2dmhTZs20kgRqrqwiHVu1apVaNGiBZo3b44nT55IwwQ+ffoUPXr0wP379/N1znOTdVjRU6dOyQXEAgMDpXtH9uMiBijfvHkjd38RW60MGzYMDRo0QN26dVGnTh3UqVMHZmZmGDx4sNwb/6zHUVlz/2LFimHlypXQ09NDYmIili5dChsbG7Rr1w5//vknJk2aJD1c6OjoYPny5dDT08OuXbuwceNGKYDo7e0tbffMmTNy9z7x/D1+/FguYNe+fXtpGMNnz55h5MiRsLKywu+//46bN29izpw5X3SsxHvokydPkJqailu3buHq1asAMq/30aNHS9dobGwsfH190apVq1wfXrW1tWFhYSF3PrLL6XtFLH9kZKTc950gCNL1cO/ePSmgVKRIEem4x8bGwtXVFY0aNULLli2xZcsWLFq0SO5eGxQUJJ3z7N3p2rVrBy0tLTx58gStWrWClZUVxo8fLxeYGDx4sBQkHjVqFExNTREWFoYRI0ZIyVJfv36NHTt2SPvz+PFjKUiT9XrN+v8pKSly+5dV1q5ZWe/XoaGhSEtLw4YNG9C4cWPUq1cPdevWRe3atdG4cWP8/vvvOHLkiNJzIHr9+jWWLFkCQRBUDgUqio+Pl/IfHDt2TO6a+Zzv04I8foDq3wVZ5/348WOOXaDEoUF79uypMnGsq6ur1ELk1KlTcHR0hJWVFQYNGoRSpUrJJcX8nO808boRf//khYZQ2G1ziIjoP+3Zs2e4du0a9uzZI31h6+jooEKFCtKPA5lMhk+fPkk/KNzc3KTmltu2bcPixYsBZGZmF4MWQ4YMQceOHeW29fDhQ0yePBlxcXGwt7fHuHHjEBwcjEGDBsk17y5VqhS2b9+OvXv34siRI9IPSi0tLVhaWmLlypWwsbGRfgwAmc2Shw0bBjMzM4wfP14uMWXJkiXh4eGBSpUq4a+//sKZM2egr6+PwYMHY9CgQbh//z6cnZ1Rp04dLFmyRG7YtClTpuDSpUuwsLDAyJEjpWb8QOaPmbVr1+L+/fsQBAEmJiaYMGGC0mHJ7t69i5UrV+Lhw4cwNDRE48aN0aNHD8yePRsGBgawtLREixYtVCbjvHjxImbMmCG3z2XKlMH48eMVEmdldfjwYbx79056y5/VqlWrpLfXXbp0gaurKx48eIA//vgDQ4YMQY8ePVC0aFHY2NjI/fgvVqwYhg0bhsaNG2P8+PFSk1AgM2HlsmXL8PjxY2zatEnurVL16tWlh8S4uDjs378fJ0+eRHBwMCpVqoTKlStj8ODB0gNHduHh4Vi1ahUuXbqEuLg41KpVC0OGDJGyu0dERMDe3l6uLmlra2P48OEYP368NM3Ozk56y6+np4cKFSrAyMgIrq6uSocDVOXcuXNwd3fHixcvULZsWZQpUwaOjo5SEtisx3nLli1yb9GKFSuGcuXK4dChQ3nu45uWlgZvb2+cPHkS/v7+0NTUhJaWFjQ0NFC/fn307NlTCr5lNW3aNIXrqHnz5pg9ezb69euHDx8+SPPq6+tjxYoVuHfvHrZt2yaVWUNDA02aNJHqC5DZpWvlypXw9/dHjRo1MG7cOLRr1w79+/fH7du3sWvXLmk4YbH8f/31F44fPw49PT04OjrC2dkZPj4+2Lp1K4YPHw47Ozu5LPK5nfOc7N27F6tXr1boOlK6dGkMHz4cnz59wubNm+X2sXr16jh8+DBsbW3lgj9FixbFjBkz0LNnT6SmpsLZ2Rnv3r1DQkICEhISkJqaKtfSTE9PDydPnoS7uzt2794t99a8dOnSOH36tMJ5DwgIwKpVq3Dz5k0kJyejbt26GD16NFq1aqWwb/7+/li2bBlu3boFfX19NG7cGHZ2dti7dy9iY2PRokULWFpaok6dOrC3t5frUqevr4/FixdLrd6AzGDtrl274O/vDz09PbRp0wYTJ06UhgFdvXr1Zx0rAFi3bh22bduGGjVqYODAgbC3t8ejR4/kkoSWL18epUuXRseOHeHk5JSnbhGXLl3C8OHDMXz4cLmhFoHM7zVV3ytLly6VhnwGMr/v7O3tYW9vj/Hjxysss2fPHinY9urVKyxfvhy3bt2CIAiwtLSEi4uLXHP47du3Y+XKlXL3vlq1auH48ePS36dOncKSJUsQGRkJCwsLzJo1C5UqVYKJiQl69+6NIUOGSN9DQOZ30fr163HkyBHExMRIwyqXKVMGGzduhIWFBSwtLfHrr79i8eLF8PX1lbvef/vtN3Tp0gUTJkyQa7FlaGiIHTt2YPny5bh69ap0frW0tNClSxcsWLBAOv9iy6a4uDikpKQotLhYt26dXJ0Sbdy4EStXrpQ73j179sTs2bMV5hXLkr01QqlSpXD16lWpXuT3+7Qgjp/YNULV74L+/fvjzp07cvs5aNAglUnDu3Tpgi1btkjXmDLJycnYsmULjhw5gvDwcJQvXx5du3aFs7Oz3DWS3++05ORkNGvWDNWrV8fRo0dVbj87BiaIiIiI6LuhKjDxo3j58iXWrl2LVatWyfU3FwQBKSkpCAkJwezZs2FnZye9GaXC0aNHD6Smpub6xp4+36dPnzBmzBisWLFCYRjctLQ0fPjwAVu2bEFkZCRWr16tnkJSvohBvVWrVsHW1jbPy7ErBxERERHRNyA1NRUuLi4YNGiQQhI8DQ0NFC1aFP/73/9gZ2f3TQxP+KObOnUq/P39c02WSZ9vwYIFcnkfstLR0UHlypXRp0+frzIkNhUMLy8vWFhYSN3r8oqBCSIiIiKib4CXlxdevXol190ku4yMDJw8eVJpFwwqWGZmZnBxccGiRYsKfWSK/6KAgAAcOnRIZR4E0bFjx2BlZfWVSkVf4vnz57h79y7mz5+f7xFGGJggIiIiou+GmNE+p6z/3ysx0emECRPg5+enkM3f398fo0ePRt26dfM1DB99vmHDhqFWrVpYuXKluovyw3n79i0EQcCaNWuwfft2hSFqP378iKVLl+LGjRvSyEH07YqLi8PMmTOxYcMGacjd/GCOCSIiIiL6LkRGRsLGxgbx8fEYO3as0qSr37P3799j4MCBUsZ9LS0tlC9fHrq6uoiJiUFsbCz69++P6dOnS8mD6evYvn07EhMTMWrUKB77ApKeno6JEyfKjWxTtmxZFC9eHJ8+fcKHDx/QtGlTrF27VmlXD/p2BAYGYs2aNRg1atRnB00ZmCAiIiKib96aNWuwdetWuVYE5cqVw8mTJ1G8eHE1lqxgJScnY9++fdLoKGlpaShbtixMTU3Rt2/fXIe6pMITHBwMTU1NVKlSRd1F+aH4+fnBy8sLjx49QkJCAkqWLIm6deuia9eusLOzy3eXAPr6rl+/jqZNm0JHR+ez18HABBERERERERGpjerMOkRERFTozMzMkJqaKjemPBEREX3bPnz4AF1dXfz999/qLsoPgYEJIiIiNUpJSfkhk/gRERH9yNLT0zlaSwFiYIKIiEiNypcvDwA4d+6cmktCREREedW2bVt1F+GHwpSyRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFERERERPSNkckEdReB6KvRVncBiIiICFi3/xrevY9VdzGIiOgb8FP5Uhjt2ELdxSD6ahiYICIi+ga8ex+LwHfR6i4GERER0VfHrhxEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBARERERERGR2jAwQURERERERERqw8AEEREREREREakNAxNEREREREREpDYMTBSyWbNmoXnz5vD391d3Ub6YIAg4e/YsevfujTVr1sh9tn79epiamuLGjRtqKl3Bu3TpEgRBAAD4+/ujefPmmDVrVqFvNzU1FUePHkXPnj0VjnNBCAwMxOzZs2Frays3/WvuY2FKSkrC7t27YW1tjVu3buU6/61btzB06FBMmzYtz9t48eIF3r9//1nl+1GO8/fm+vXrMDU1xYYNG9RdFCIiIiLKhoGJQhYeHo64uDjEx8eruyhfRBAEzJ8/HzNnzsT9+/cVPn///j0SEhIQHR2thtIVrKSkJMyZMwcaGhrQ0NAAAMTHxyMuLg7h4eGFum2ZTIZ169ZhzZo1+Oeffwp8/SdPnsTkyZNx4MABpKamyn32tfaxMIWFhWHWrFlYuHAh3r17l+v869evxx9//IErV65IQai8qFGjBjZv3owTJ07ku4w/wnH+HsXExCAhIQERERHqLgoRERERZaOt7gL86NavX4+4uDgYGhqquyhfRENDAzNnzkSFChWwdOlShc9nz56NUaNGoXz58l+tTPfv34eJiUmBrjM5ORkjRozAuHHjYGZmJk1v0qQJrly5gpIlSxbo9rLT1NSEi4sLDAwMsGDBggJfv62tLZo2bQpLS0uFz77WPhamSpUqYenSpYiNjcXly5dznX/UqFGoXr06XFxc8rUdXV1dzJgxAxMmTEBsbCwcHR3zvOyPcJy/dcruDR07doSZmRnKli2rplIRERERkSpsMVHIdHR0vvugRFalS5dWOl1TU/OrBiUCAgJw8ODBAl/vrFmz0KBBA7mghMjQ0BA6OjoFvk1lihcvXmjrLlWqlMrPvuY+Fqac9jE7VXU6NxoaGpg3bx42bdqktBVRTn6U4/wtiomJwcaNG5V+Vr58eWhq8muPiIiI6FvDX2iUL1paWuouAjIyMuDm5pavpvd5ceHCBfj5+cHJyalA1/s5CvPhSVv7x28olZ99/JI6XaJECQwcOBCurq4KXWNIPRYsWIDExER1F4OIiIiI8oGBCfquyGQyuLm54ebNmwW6XkEQsHz5cpiZmaFMmTIFum76sXXq1Anv3r2Dt7e3uovyn7dp0yb4+PiouxhERERElE8//qtTNYqIiICnpycOHTqERYsWwdzcHCkpKbh06RL8/Pxw8eJF3L9/HydPnsSGDRsQFBQEc3NzLFmyRKH/eUhICJYvX46nT5/i06dP+PDhA4DMZuhVqlRBp06dMGTIEAQFBWHTpk0ICwvDpk2bMG3aNFy8eBHTp09Ht27dAGSOyrBu3Tq8e/cOAQEB+OmnnzBy5Ei0b99ebpvJycnYsGEDTp06haJFi6JkyZJo0KCBwn7GxMTAx8cHHh4eGD58OBwcHOQ+v379Onbt2oWPHz8iMDAQTZo0wcSJE1G7dm0IgoC7d+/i5MmTOHnyJJYtW4ZSpUph8eLFuHfvHqpVq4aFCxeiTp06AIDly5dLQYkLFy5I2/L09ISWlhYuXbqE9evXIykpCYGBgUhJSUH37t0xf/78HM/VzZs38fLlS7i6uip8JpPJcPnyZXh4eCA5ORk7duwAALx+/Rp+fn44efIkbGxs0KdPHyxduhSnT5+Gnp4eJk+ejM6dOyusz8vLC56enkhNTUVsbCwsLS3h4uKSY0Dk0qVLmDlzpjQSxK5du2Bubo7w8HD06dNHSvS4YMECheN/7tw5bN68GYmJidDU1ETfvn0LfR89PDywb98+ZGRkIDAwEOnp6QAAIyMjFCtWDFu3bs0xx0JgYCBWrVqFsLAwvH//Htra2rC3t8fIkSMVWjiEhYVh2bJlePToEYoUKYI6deqoTDabkZGBHTt24PDhw9DW1oauri6sra1VHvO81KXy5cujSpUq2L59O3r37q1ynwD11aUbN25g27ZtSEhIQGhoKGrWrInhw4fDwsJCmkcQBJw7dw7r169Hv379UKdOHUycOBEymQw7duxAkSJF4O7ujosXL8LX1xdHjx7F6tWr8fHjR1haWmLWrFmoVKmS3HYTEhKwadMm3L59G4mJiUhMTMRvv/2GkSNHQl9fX5rv/fv32LZtG65evYpjx45hwYIF8PLywpAhQzB69GjIZDIcOHAAhw8fRkZGBsLDw6Xy1a1bFwCwc+dOHDt2DADw+PFj6TpYv349ypQpg7Nnz2L//v2oUqWKlL/l6dOnOHHiBE6ePIlRo0ahWbNmWLx4Ma5cuYJy5cphzpw5CvlY0tPTsXXrVvj6+kImkyEwMBAymQwAUK9ePejr62P37t051gMiIiIikscWE4XkzZs32LFjB7Zs2YKwsDBpemBgIPT09ODn54fExERs3rwZoaGhmD9/PhwcHHDx4kWF5JLv3r1Dz549ERcXB19fX1y+fBn29vYAgGLFiuHgwYMYMmQI1q1bh27dusHLywvp6elYvHgxLl68iISEBBw6dAgA8OzZM0yePBnjxo3Dvn37cObMGejr62PMmDFybxpTUlIwZMgQ3Lx5E/v374ePjw+cnJykBylRZGQktmzZgs2bN+PNmzcKx8HLywv79+/H0qVLcejQIRw4cABPnz6Fo6MjAgIC8P79e2hqauLSpUv48OEDLl68CG9vb0yYMAHTp0+XggVit41JkybBzc0NANCmTRt4e3vD29sbWlpaePz4MaZPn44VK1bg6NGjOH/+POrXr5+n8+Xn5wcAMDY2Vvhs//792L59Oy5cuICMjAwAmQ+YERERePHiBV6+fImgoCAsWbIEdnZ2WLVqFTIyMjBt2jSEhobKrWvmzJnw8PDA+vXr4e3tjXnz5uHgwYMYNGgQ0tLSVJavdevWuHDhgkJgqGLFijh//jw6deqkdLk9e/bA1dUV48aNg6+vL7Zv3459+/YV6j4uXrwYc+bMwfTp03H8+HF4eXmhaNGiAID+/fvj4MGDOQYlIiIi0KdPHxQpUgT79+/H2bNnYWZmhjVr1ig88IWEhKB79+7Q09PDiRMn4OPjg0qVKuHcuXMK6xUEARMnTsTBgwexYcMG+Pj4YNasWdiyZYvCvPmtS0ZGRggKCkJAQIDKeQD11KV9+/ZhypQp+OOPP+Dh4YETJ05AR0cHgwcPhqenJ4DM63jo0KEYPXo0njx5gvj4eLi4uCAoKAiBgYHw8vJCly5dsHnzZnz69Al+fn6YPXs2EhMTkZSUhHPnzmHgwIH49OmTtN2YmBj07t0bqamp2LdvH3x9fTF79mzs3LkTjo6OiI2NBQAcOHAADg4O2LFjB5KSkuDu7g4vLy8kJCTgwIEDAIBVq1Zh/vz5WLhwIby8vLBz5078/fffGD58OJKTkwEAAwcOlIYCrV+/vnRvqFixIrZt24atW7fi9u3b0r0kISEBiYmJ+PvvvxESEoJHjx5h8+bNGDBgABYuXIj379/DxcVFoVvIpEmTsHHjRqxatQp+fn7YsmWLNHqPq6srgxJEREREn4GBiUJSo0YNTJ06VeFtm7GxMVq2bCm90WzevDmGDBmC+vXrw9XVFdra2rhw4YLcMmvXrkVUVBSGDh0KXV1daGpqYtKkSdDW1kZYWBhCQkIAAKNHj8a6desAAMHBwahWrRouXbqEYcOGwdnZGQAwdepUjBgxAj///DMAoGTJkhg8eDAAYMWKFXLbvH//PpYuXQoDAwMAmQ/HdnZ2cmUzNDTE5MmT0bFjR4VjEBERgXnz5sHNzU1K5lizZk3Y2dkhMTERGzZsQIUKFdCkSRPUq1dPWt/06dPRsGFD9OzZEw0bNkRAQACCgoJyPeZ+fn4wMDBA5cqVAQBly5bFzJkzc10OAO7evQsAqFatmsJnffv2xR9//CE3TVNTExYWFmjevDmAzCEg58+fD0tLS7Ro0QIODg5IS0vD1atXpWV8fHzg6emJuXPnSuff3Nwc+vr6ePHiRa4Ptdra2qhZs6bSz2rUqKEw7eXLl1i4cCGcnJzQokULAJnHN/u+FOQ+hoSEwN3dHU2aNJGWq127tvT2+tatWznuIwCcPXsWkZGRMDc3h4aGBjQ1NdGrVy8AkOvCIwgCJk+eDH19fcyePRtaWlrQ1NTE+PHj8dNPPyms98CBAzh58iTc3Nyk+t+gQQMMHDhQYd781qUqVaoAAK5cuZLjvn3tuhQQEIAFCxZg2LBhqFWrFoDMxKqLFi1CiRIlMHfuXLx+/RqGhobYtm0bfv/9d2n9W7ZsgYeHBxwdHdGnTx8p2WxiYiKuXr2KK1eu4Pr161i7di10dHQQFBSEXbt2SWVcsGABoqOjMWnSJKmVS+vWreHk5IQXL17g//7v/wAAvXr1goeHB4DMYEFcXBxu3LiBCRMmYOLEiQAyz1358uXxyy+/AABq1aoFMzMzfPjwIdfrBgBGjhyJkSNHyk0rXrw4zMzMYGpqCiCzPrm5ucHMzAw2NjawtrZGTEyMXGLT27dvw8/PD+3bt5eOZ8uWLWFlZSV9TkRERET5x8BEIVM1uoKuri4AoGHDhtI0fX19GBgYICoqSm7eBw8eAAAqVKggTatQoYL00BQdHS1NF5tS6+npoV+/fihevDgmTZqE1q1b4+HDh3jx4gXWrl2Lnj17Sv9Wr16NChUqQBAEREdHIy4uDrt370bDhg2lBziRquE5le2nr68v0tLSMGLECLntXb58GRUqVJC6JWQ9HtlbBIgPmJGRkUq3m5VMJoO/vz82btwoNa02MTGRjpMq6enpUmsPVSM05HYe69WrJ5ewUlm5d+zYgQoVKkhNz4HMh1J3d3csXLhQaWuN7MQ3s3mxadMmpKWlKbSmaNKkidL5C2IfHz16BJlMhooVK8qto3HjxgAy36Lnpm7duqhRo4bctSE+fGd9e33t2jXcv38fNjY2cskuNTQ0pO2JMjIysHHjRpQrVw7m5uZynymr0/mtS+KINMpaDWX3NevSnj17kJqaqrDPBgYG6NixI9LS0rB3715punjefvvtN/z8889o2LAh5syZgzJlykifaWpq4s8//0SJEiUAAO3bt0e/fv0AAKdPnwYAfPjwAb6+vmjSpInC6CN9+vSBhoYGfH198fHjRwD/3rcEQYCzszN0dXUxcuRIdO3aFQBgYWGBNm3ayK1HWZ3ISX7uxYDy4y7ei1XV76z3YiIiIiLKO+aYKGSqMv6rGnVBV1dX6o8vEoc+jIqKkt4YApk/zIODg1GuXDmF9ZYtW1bhIfbRo0cAgGXLlql88w4AJ0+eRFJSktK38OKP+OyU7eejR49QqlSpPA3rmdPxAKBwTJTp3r07PDw8sGLFChw/fhwjR46Era0tRo8eneNycXFxUrP6rP3e81K+vJY7KSkJz58/V3j4AYBGjRqhUaNGOZYxv2QyGS5evAhNTU1Ur15dadmy+9J9BCB10cgeXBMfIrPWVVVMTExw8uRJaT1eXl64fPkyAMiNxHL+/HkAyluLZN/Hp0+fIiwsDM2aNct1XiD/dUlPTw9A3gJoX7MuiS04xFZPWZmZmcHDw0OuRYB4HZctW1ZhfvF+UrRoUYVjZmdnB3d3dwQGBgLIDBplZGQo3W758uVRrVo1BAYG4uHDh7C2tpa2W6JECanbT1ZZW3PduHED3t7eUusbMXCUm4Ko31nvxVnlp34TERERkSK2mPgOdOnSBcC/3Q2AzAe00NBQ1K5dW2pGnpu4uDgAkBIlqhIcHAwAKFKkyOcUV257UVFRX23ovpo1a8LT0xNNmzbFy5cv4eLigp49e0oPS6rkJejxpeLi4iAIgvSGuLBFRUUhISEBOjo6hTr0aHbNmjVDpUqV8PTpU7nzLuZHaNeuXZ7WExsbi7lz52LMmDGoVauW1Ow/K7GeKnuQzU7s7pTXOp3fuiQ+xH6Nup6fuhQeHg4ASpOBim/9k5KSvrhMYnBIDPBFRERIZVVGbP0l5ofIiwsXLqBbt264fv06Jk2apNBN7mto164d9PX1cffuXbkgWX7rN9GPIO7Da/jf3IfQF5eUfi7LSEf4y6t4dmkrXlzdgedX3PHm7mGkfIpSOj8AJMd/RNA/x+F/QzEXkiqyjDQ8u7gFL67uzHXepLj3CP7nBIIeHEP4y6tIjA3P83aIiKhwMTDxHXB0dISTkxN27tyJly9fAgD27t2L2NhYzJkzJ8/rEd/qim+fswsICEBqaqr0ICsmp/tcenp6kMlkcn3js3r+/PkXrV+ZWrVqYc+ePdi8eTNq1aqFR48eYdCgQTk+fBkYGEhvg/PzoJQfYkuM8PBw6aEtu7z0lc8r8RympKQUyINnXunq6mLjxo0oXbo0li1bhoyMDHz8+BG7du1C27Zt8/TgFhISAnt7eyQnJ2P37t1o06aN0i4s4j7mpXuIuHx+6nR+6lJKSgoA1d0FClJ+6pJYnqwJeEViF4vcujrlhXguxC4t4nbFwIiqbWcfxUOV5cuXY/LkyZg3bx5cXV3lurV9TYaGhli/fj3i4+Oxbds2CIKAoKAgeHl5oX///tLoQUQ/spTEGIS9vILgf44jLuIVkCVIJxIEAa9u7kNU6FPUMu8F45aDYNxyEIrolcazi1uQnCDfukwmy8D7gFsI+uc4PgbehSxDdTLo7IIe+CIxVvEeJ7f+jHQEPzyJ13cOwbBqI1RrbIeKRi2hV6pijssREdHXw8DEd0JbWxvVq1fHtGnT0LVrV9y8eRP79u1TmfNBmdq1awPIHFrz9evXCp/v3LkTurq6UncRsevH5xK3t3r1aoUH/k+fPuHIkSNftP7sVqxYgdTUVACZSfYOHz6MVq1aISwsDNeuXVO5nI6OjpRbQtUwk1+qePHiqFq1KjIyMpRm7b906ZLSh8fsxAe67G+ixTfV4n8NDAykZvSPHz/+orLnl9jUPzQ0FF27doWzszPs7e2xcuXKPC0/f/58hIeHY/LkySq7QgHIVz0V5/X3989T8Cm/dUlsKaEs6WZBy09datq0qTQtOzGgIyZuzCtlXSfEVlji9szMzABknpvs3R7EbRsYGCgdfji7x48fY9OmTbC3t/8mHvyLFi2KUqVK4datW+jSpQumTp2K0aNHY8aMGeouGtFXUUSvNCoZtcJPdVUHmuM/BiL+YyAqGbWETtHMfDQaGhqoVLs1ZLIMfAi8Kze/pqYWytc0R81mPfJVlvCXV6X1qyJLT4P/9d34FP0Wta2GQN+g8O/TRESUfwxMFLKsQwJmJQ7np6p/dNbphw4dwv79+7Fz5054eXnBx8cHa9euVTp8odi8WFn3BDMzM1SrVg3JyclwcnKSRjhISkrCkiVLpH7lLVq0gKGhId6+fav0gSZr+XPazy5dukBbWxv+/v4YPny41PQ+IiIC48aNk3sgys/xEB/Os+9jfHw8Tpw4If2tq6sLR0dHuWVUEQM8qrq5iNvPXj6xDHkpd7du3QAA7u7uOHLkiHSurl69ijVr1kijMgCKgQaReI6OHz+OjIwMZGRkwNPTUxruVCy/hoaGNIJK1uSGWYkP3gW5j6mpqRg+fDi6du2KDRs2wNfXF4cOHcKIESNU5rbITqwnWeuYWFZxW4IgoHPnzgAyEy6q6tYgrsPY2BjGxsZISkqCt7d3jvMC+a9LYiAgazJKVb5mXRJHHPH19ZUbyhMA7t27B0NDQynBJPDv/SN7vcsqNjZW4fo/f/48NDU10b9/fwCZx9vS0hLp6enSUMWihIQE+Pv7Y+DAgVLS0pzuW2J9yF5fs9YJcXlV9wZxvqz/FeXnuEdFRWHEiBEYO3YstmzZgqNHj8LDw0NK6En0X6Klo7obXUZa8v//r/x1q6GhCS1tXWjpKO9Wp6VTLM/bjw1/iYTodzkGSAAg4I4nkuLeo2aznjmWmYiI1IuBiUKUkZGBV69eAYBcC4UPHz5IbxHFrhlA5ltw8S1m1um3b99GXFwcWrZsCRsbG/z222+ws7NDt27d4OrqiocPH0rzik24/f395Ua9ADIT2y1cuBDFihVDaGgoBg4cCBMTE5iamuLy5cvSkKJFihTBX3/9BS0tLUybNk164x4XF4djx44ByMxO//btW+nh4MWLFwr7WbVqVUyZMgVA5jCR7du3h5mZGaysrFC5cmVYWFgA+HcEBLHcWYm5AbJO//nnn6GhoYGAgAAIgoBz585JD6bz5s2TEv4JgoBLly6hRo0acg/9yrRs2RIAVA5LKnY7ybrPWadnL7f4MJV1+pAhQ2BiYoL09HRMmTIFFhYWMDc3h7OzM2bOnCk3soR4PLN3dxGDOX5+frC0tISFhQXu3LkjBSE2b96MyZMnIyMjA+PGjUP16tXh5+cnN7rEvn2ZfXejoqLw7NkzfPjwocD2MSIiAkFBQVixYgXatWsHW1tbdOzYEfb29ujfvz82b96s8ICZnZi8cdasWXj48CE8PT0xb948AJn14cKFC7h69Srq1auHIUOGICEhAWPGjJESTwYEBEiJEe/cuSPVoXnz5qFYsWJYvHixVEdSUlLg5eUFAHjy5AnevXuHhIQEaf681qU3b95AW1tbqkc5+Zp1yczMDM7OzoiOjsa0adOkbij37t3DoUOHsGDBAimhIwDpfnX9+nWV5U9MTMSyZcukB/rHjx9j69atGDNmjFxgZv78+Shfvjw2bNiAGzduAMgMJvz5559o0qQJhg4dqrDdDx8+KOx//fr1oaWlhaNHj+Lo0aO4e/cuZsyYgX/++Ufal40bNwLI7Gqhr6+PoKAgpKam4v79+9K9VDy+b968kcsPIV5reTnuL168QExMDKZMmYL27dtL9btLly4YMmQI9u3bJ7duoh9ZTsG44oZVoaGljbAXl5Ce8m/uncSYMGhoaqN89ab5XmdWSfEfEO5/DTVMf89xmY9BDxD3/hUq1LKAbrGSeVo3ERGph4bAX1GF4tmzZxg1apSUFE1HRwe///47LCwsMGfOHKmvu76+PgYOHIiff/4ZS5YskQIW+vr6GDVqFIYOHYrw8HDpTWRUVBSSk5Pl3gjq6uriyJEj2L59O3x8fKS3mfr6+hg7diwGDx4sV7bnz59j+fLluHPnDrS0tGBtbY0//vhDyiwvun79OpYvXw5/f3+Ym5ujevXqKFOmDPbs2QMLCwu0aNECzZo1w+DBg6WkgJqammjRogW2bt0qrefUqVPYtGkT/P39YWBggB49emDUqFHQ0tJCcHAwBgwYIL1x1tbWRvPmzTF16lSMHDkSb9++laa3bdsWq1evBpD5AL5x40aYmppiwIABaNWqFdzc3KTWARUrVoShoSHq1KmDiRMnwtDQMMfzFRUVBWtra3Tt2lUhb8fs2bPh4+Mj5REoX748Nm/ejLlz58qNaFCtWjXs2bMHzs7OePbsmRQIqFatGg4fPgx9fX0kJiZizZo18PX1RWxsLBo3bgxXV1e54S0HDBiA27dvSw84VapUwc6dO6Ukp7t27cKGDRsgCAJ69eqFcePGYf369bh9+zYmTJgAU1NTaV2RkZFYvHgxzp07h4oVK6JBgwbo2LEjRo0ahSZNmqBFixbo0KGDVHcKYh/XrVsHDw8PyGQyxMfHIzU1Ve5hzcHBAQsWLFB5LiIjIzFt2jTcuXMHlStXRu/evdGrVy/06NEDHz58wOjRo9G3b19p/t27d2PXrl2Ii4uDhYUF6tSpgxcvXuDZs2do0aIFWrVqhdatWwPIDD4sWrQIDx48gImJCWrUqIE6depg2bJlaN68OSwtLWFra4vVq1fnuS5lZGSgefPmaNq0KdavX69yv9RRl0RHjx7Fzp07ERERgSpVqqB8+fJwdnaWAgmRkZHo0aOHXIuhypUrY+fOnahatao0zdjYGJUrV0bPnj3h5+cHmUwGHR0dDBw4UK7lhej9+/dYuXIlLl26BENDQ+jp6aFdu3YYOHCg1LphzZo12Lp1q9TFpkiRIujTpw/++OMPaT1eXl5YtWoVUlJS0KxZM7i4uODx48eYPXs2mjRpgnnz5km5Mg4fPoyFCxfil19+Qf/+/dGxY0cMHz4c165dk+6bP/30E3bv3o3BgwdLwUgNDQ388ssv2Lt3LxwdHaWhXzU0NNCgQQN4enpCEATMmjUL165dQ3JyMhISEhQCbWPHjsWYMWNyrAfZtW3bFgBgbu+KwHcccpS+D/EfA/Hy2i5UMrZC5dq/Knwe9fYx3tw7jKLFy6Jms17Q1imK0OcXUdGoZY5BgrtH3FDcsBqMWw5U+nl6ahJe3fJADdPfUUSvtMplBEHA47NrkJoYixqmvyM+MggpCZFIT01EqQr/Q0WjVtDSzltLPiJ1qP6TAf5vfEd1F4NyIH5/nzt3Ts0l+TEwMPEdOHToEIKCguDq6io3PTU1FW/fvsWff/4Ja2trhQAE5c/ChQtx9uxZnD17Vt1F+W7Fx8djxIgRWLVqldzQiWKQ4sKFC/jrr7/kRpj53t27dw+Ojo7w9PRUOoTnj8TY2Bg//fSTNFTrf01YWBhcXV2xfft2udFgZDIZoqOj4eXlBV9fX/j6+uZrvQxM0Pcot8AEAMRG+CP4nxOQyTJQtHgZVDfpgiL6isMIZ5VTYEIQZAi4fRAV/9cCxcv8nOMyCVFv8eLKdugUKY6fG/6G0pWMAWjg/evbePv4FPQNfoJRi4HQ1NJW2A5RYUpPzVui9aqVDTB3tE2+1q1q2HsqHAxMFCzejb9xz58/x19//SU1Kc9KTFTZtm1bKXkjfb4RI0bAz88P165dQ4sWLdRdnO/SrFmz0LBhQ7mgBJDZkqZUqVLo2rUrdu7MfUi378mBAwfQrVu3Hz4o8V8nk8ng4uKCTp06KQxRq6mpCUNDQ/Tu3VvlKETijxdlwsLC8jxCCdH3RJaRhv9Z9EVGegoCbh/Es0tb8ItZd5Qs/8tnre/t4zMwqFxXLiihSlJcZnfWkuVrwqDyv4lzK9Q0R1yEP+I+vMbHoPso/4vybiVEhcVz1dDcZ/r/lk3N37r5vpm+Z8wx8Y3bvXs3MjIyVI5OIAgCXr58iQ4dOnzlkv14DAwMsHr1aixZskRqak95Fx4eDj8/PxQpojypGZCZa6RHj/xlXf+WPX/+HM+fP8e0adPUXZRCpyqR73/FvXv3cP/+/Rzr97lz5zBkyJCvWCqib1e4/3UkxoSjaImy0Df4CbWthkBbVw+vbu3Hp+jQfK/vU0woPry5g7CXV/D43Dq5f5mfv8Pjc+vw5q4PACA9NTO3hXYRxTfIhlUbAwDiPyiOUEZEROrBFhPfOGtra/j4+MDZ2RmTJ0+WeysbExMDT09PDB06FHp6emos5Y+jUaNGmDBhAubMmYN58+blOFwlyStXrhxMTEzg7u6O0qVLo1evXnL18saNG3j9+rVcfojv2YcPH7By5Ups2rQJJUrkPFzdj0BMbBsZGYmPHz9KI8T8VxgZGaF69epYtmwZNDQ00KVLF2mkGZlMhnPnzkFLSwu//vqr0uVzauaZU2sKou9RUlwE3j09h9pW/wbqdIuVwv8s++HZhU14+/QMjFsozyGhiiw9FYIgQ0pCpNLPhYx0pCREQqdIcQCAtm7mCB+yDMVReooWz8wVJJMpfkZU2HqM35r7TPi8rhxE3zMGJr5xbdu2hZeXF9zd3TF+/HgkJiaiRo0aqFq1KurUqYM+ffqwP1kB+/XXX1G5cmWsX78eY8aM4TCAeaSlpYVdu3bhwIEDOHbsGNavX4/SpUvjl19+wc8//ww7O7sfJijh7++PkydPYunSpShevLi6i1PoXF1dcfLkSQCZuW1sbW0xaNCgfCd5/J6VLFkS3t7e2LVrF/bt24fFixejXLlyqF69OqpVq4Zu3bqhVq1a6i4m0Tch7v1rAAI0teSHVy6iVxoGVeojKuSh8gVzUKJsdZh2ma30M2U5JoobZibuTYxV0jrj/3+vFytRTvEzokKmrZu3YWt1ixTlb3z6T2Fg4jtQu3ZtLFq0SN3F+E8xMjJCrVq1GJTIJ11dXfTv318aReZHVbNmTYwdO1bdxfhqli1bhmXLlqm7GGqnr6+PkSNHYuTIkeouCpHa5dSXXUxw+Sn6HYqVLJ99Qej+/9E0VK/zy/vJFytRDiXL10Tc+9dIjv+IoiX+beWVGBMGDQ1NlK3W5Iu3Q0REBYM5JohU0NTk5UHKsW4Q0X9dWlIcACD1//83q1IVjVG8bDWEPr+IpLgIaXr8x0BEvXuMKvXaKV1nalLmUOppyQkQCiCfTbVGdtApWgKB948gIy0zd1RyQhRCX1xClXrt5YIVRESkXmwxQURERER5kpoUh9d3PJEYmxlwiAx+gMSYMFSp1w4ly9cEAGhoaOB/5n0Q9vIyAm4fgpa2LrR0i0JLuwiMWw6GXqkKCusNuHUACVEhAICUT1F4fG4tylU3RcX/ff4oWbp6pVDbagjePT2P55e3QbuIHjQ0NFG1gTh8KBERfSsYmCAiIiKiPNEtVhK1rZxynU9TWwc/1W2Ln+rmLblrTfNen10mVbkngMzy1jDt+tnrJiKir4PtkYmIfmBPnjyBi4sLBg0apO6i5Gj9+vUwNTXFjRs31F0UIiIiIvrK2GKCiOgHtX//fuzZswevXr1Cs2bN1F2cHL1//x4JCQmIjo5Wd1GIiIiI6Ctjiwkioh+Uo6MjFixYoO5i5Mns2bNx5coVdOzY8YvXdf/+/QIoERERERF9LQxMEBH9wEqVKqXuIuSJpqYmypcvn/uMuQgICMDBgwcLoERERERE9LUwMEFE9APT0tJSdxG+moyMDLi5uUEQBHUXhYiIiIjygYEJIiL67slkMri5ueHmzZvqLgoRERER5RMDE0REP5BDhw6ha9eu6NKlC37//XecP39e6XzXr1+Hs7MzunfvDjMzMwwfPhzPnz+XmycgIABDhw5Ft27d0LRpUxgbG6N9+/YK67p79y6GDRsGBwcHWFtbY9iwYXLrSk9Ph4+PD2xsbHDr1i1cv34drVu3Rvfu3ZGQkICYmBjs2LEDtra28Pb2lpb79OkTNm/ejObNmwMArl69is6dO6NRo0YYMGAA/P39pXmXL18uBSUuXLgABwcHODg4ICMjQ5rnxo0bGDp0KHr37g0rKysMHjxYYRQQQRBw9uxZODg4wNvbG8+ePcNvv/0GGxsbhIWFAQAuXbqEXr16wd7eHg0bNoSxsTFmzJiRp/NDRERERIo4KgcR0Q9i0aJFOHLkCLZu3Yq6desiODgYAwYMUJjPy8sLFy9exNKlS1G8eHEEBARg4MCBcHR0xKFDh1CzZk0kJCRg6NCh+PPPP/Hrr78iNTUVc+fOxe3bt+XWdfjwYSxZskTaZnR0NDp06IC+ffvCy8sLGRkZmDFjhpSQ8t27d1i5ciUiIiIQHh6Oy5cv48mTJzh8+DAiIyOl9V68eBELFixAYGAgAODevXsYO3YsihcvjuTkZNy6dQv9+vXD0aNHUaFCBUyaNAmtWrXCgAED0KZNGyxcuFCunPv27cOGDRvg7u6OWrVqISEhARMnTsTgwYPx119/oUePHoiMjMSUKVNw9epVAEB8fDxcXFwQHByMjIwMXL16FXXq1MH06dPh6emJypUr4+PHjxgxYkRBnkYiIiKi/xy2mCAi+gFcuXIF27dvx6RJk1C3bl0AQNWqVTF27Fi5+SIiIjBv3jy4ubmhePHiAICaNWvCzs4OiYmJ2LBhA4DMVhChoaFo2LAhAEBXVxczZ86US6YZGBiI2bNnY/jw4dI2DQwMYGJigoSEBFy6dAk1a9aEh4eHNFypj4+PFDwZMGAA2rZti8mTJyuMxvHrr7/Cx8cHenp6ADJbgpw8eRJXrlzB3r17UbJkScTExGDNmjW5HpuAgAAsWLAAw4YNQ61atQAAxYsXx6JFi1CiRAnMnTsXr1+/hqGhIbZt24bff/9dKuuWLVvg4eEBR0dHtG3bFn5+fjAwMEDlypUBAGXLlsXMmTPzepqIiIiISAkGJoiIfgDr1q2DlpYWbG1t5aabmJjI/e3r64u0tDSMGDECPXv2lP5dvnwZFSpUwPv37wFASiA5e/ZsxMfHAwCKFSuGX3/9VVrXvn37kJqaijZt2shtY86cOZgzZw66du0qTatYsSIAoFevXjAwMECrVq0wY8YMFClSBACkIElWxYoVQ+nSpQEA06ZNQ4UKFQAAZmZmGDduHADgzJkzuSa73LNnD1JTU2Fubi433cDAAB07dkRaWhr27t2rUNbffvsNP//8Mxo2bIg5c+agTJkykMlk8Pf3x8aNGyGTyQBkHmMxUEFERERE+ceuHERE37moqCjcv38fP/30k9TCQKSrqyv396NHj1CqVKlch9S0tLRErVq1cObMGdy9exdOTk5wdHTEmDFjpHnE7hnig7yocuXKcHR0lJsmjg5StmxZpdtTNXqIhoYGAKBEiRJy0+3s7DBv3jzExMQgOjoaZcqUUbkvV65cAZAZiMjOzMwMHh4e0r7kVtbu3bvDw8MDK1aswPHjxzFy5EjY2tpi9OjRKrdPRERERDljiwkiou9cSEgIAEitD3ISFxeHqKgoJCYm5jifrq4u9u3bh+7duyMmJgZLliyBjY0NLl26JM0TGxsLAPj48eMXlP7zGBgYSK0pxJYLqoSHhwOA1PIjKzGokpSUlKft1qxZE56enmjatClevnwJFxcX9OzZU8qFQURERET5x8AEEdF3TmxVIAYKcqKnpweZTCYleMwu62gapUqVwvz58+Hr64tff/0VHz58wKhRo/DkyRMA/3a/+Oeff5SuKyAgIF/7kV+amprQ1dWVAhSqiOUUR9XISkdHBwDy1RWjVq1a2LNnDzZv3oxatWrh0aNHGDRoUJ6DG0REREQkj4EJIqLvXPXq1aGpqYnIyEiEhobmOG/t2rUBAKtXr0ZycrLcZ58+fcKRI0cAACdPnsSDBw8AZD6Ib9q0CWPHjkV6ero0T506dQAAO3fuVNjOx48fce7cuS/ar6yyt4pISkpCVFQUTExMoK2dc6/Epk2bAoBcaw9RTEwMAMDKyipP5VixYgVSU1MBAK1bt8bhw4fRqlUrhIWF4dq1a3laBxERERHJY2CCiOg7V7JkSSkpZdYkjlmlpaUBALp06QJtbW34+/tj+PDhCA4OBpA5Wse4cePkHtAPHDggt45BgwYBgBQIcHBwAAA8ePAA8+fPR0pKCgDgzZs3GDNmDNq2bSstKyaozMjIUFo+cbqqbhkfPnyQ+/v8+fMAgIEDB0rTxNYP6enpcvOK8/j6+uLTp09yn927dw+GhoZyiTpzKmt8fDxOnDgh/a2rqyvl0xC3T0RERET5w8AEEdEPYMaMGShTpgzc3d1x+PBhAJkP1mJw4fXr1wgODkbx4sUxZcoUAMCtW7fQvn17mJmZwcrKCpUrV4aFhYW0Tm9vb2zdulUKFpw/fx5FixaVAhKmpqYYMmQIAGDXrl1o3rw5rKysYGtrC1tbW9SsWRNAZrDh9evXAKCyVcGLFy+kciqzaNEiqYVHcHAwli9fDgcHB7ngx88//wwNDQ0EBARAEAScO3cOHz9+hJmZGZydnREdHY1p06ZJXS7u3buHQ4cOYcGCBXLDoL569QoAcP36daVlmTdvnpRQUxAEXLp0CTVq1EDz5s2Vzk9EREREOeOoHEREP4AqVargwIEDWLhwIdzc3LBv3z4YGxvD0tISenp6MDExwYULF2Bra4uBAweiYsWK2LRpE/z9/aGnp4eBAwdi1KhRCutdsmQJNm3ahKpVq8LAwAAeHh6oVauW9PnUqVNRtWpV7Ny5E2/fvkWJEiUwceJEqQXCs2fPMGzYMKnFw9atW3HmzBkcPXoURYsWRWRkJPr06SMlj3R3d8fLly+xdetWuXJYWFigd+/eyMjIgEwmQ79+/eRaSwBAuXLlMHHiRGzcuBHDhw/HgAEDpJE1XFxcULNmTezcuRPt27dHlSpVUL58eWzduhV169YFAERGRqJHjx549+4dAODEiRN48OABdu7ciapVq0rbiY+Px9ChQ1GxYkUYGhqiTp062Lt3b56SjxIRERGRIg0htwHgiYiI1MTa2hrv3r2TWlT8iMRWH+b2rgh8F63m0hAR0beg+k8G+L/xHdVdDMqB+P1dkDm1/svYlYOIiIiIiIiI1IaBCSIi+mbllhSTiIiIiL5/DEwQEdE3KTo6GtHRmV0bXr58qebSEBEREVFhYWCCiIi+OUuWLEHr1q2lIUh79+6N2bNnq7lURERERFQYOCoHERF9cyZPnozJkyeruxhERERE9BWwxQQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGqjre4CEBEREfBT+VLqLgIREX0j+J1A/zUMTBAREX0DRju2UHcRiIjoGyKTCdDU1FB3MYi+CnblICIiIiIi+sYwKEH/JQxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0RERERERESkNgxMEBEREREREZHaMDBBRERERERERGrDwAQRERERERERqQ0DE0REREREBAAQZDJ1F4GI/oO01V0AIiIiAt4c24KkyDB1F4OI/sOKGVZCDbth6i4GEf0HMTBBRET0DUiKDENSRLC6i0FERET01bErBxERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpDQMTRERERERERKQ2DEwQERERERERkdowMEFEREREREREasPABBERERERERGpTb4DE7NmzULz5s3h7+9fGOX5qgRBwNmzZ9G7d2+sWbNG7rP169fD1NQUN27cUFPpCt6lS5cgCAIAwN/fH82bN8esWbMKfbupqak4evQoevbsqXCcC0JgYCBmz54NW1tbuelfcx8LU1JSEnbv3g1ra2vcunUr1/lv3bqFoUOHYtq0aXnexosXL/D+/fsvKeYXyc99JTU1Fd7e3rCzs4O3t/dXKF3B+pHuod8TJycntGnTBpGRkeouChERERFlo53fBcLDwxEXF4f4+PjCKM9XIwgC5s+fj2PHjiE6OhotWrSQ+/z9+/dISEhAdHS0mkpYcJKSkrBo0SJYW1tDQ0MDABAfH4+4uDiEh4cX6rZlMhnWrVuHEydOIDg4GK1atSrQ9Z88eRLbtm3Dw4cP8dNPP8l99rX2sTCFhYVh2bJl8PPzQ3p6eq7zr1+/Hp6enggNDcXvv/+e5+3UqFEDixcvRpMmTdCxY8cvKfJnyet9JSYmBkuXLsXx48eRmJj4lUpXsH6Ue+j3JiwsDFFRUUhKSlJ3UYiIiIgom3wHJtavX4+4uDgYGhoWRnm+Gg0NDcycORMVKlTA0qVLFT6fPXs2Ro0ahfLly3+1Mt2/fx8mJiYFus7k5GSMGDEC48aNg5mZmTS9SZMmuHLlCkqWLFmg28tOU1MTLi4uMDAwwIIFCwp8/ba2tmjatCksLS0VPvta+1iYKlWqhKVLlyI2NhaXL1/Odf5Ro0ahevXqcHFxydd2dHV1MWPGDEyYMAGxsbFwdHT83CJ/lpzuK1mvi9KlS2PevHnQ0tKCh4fHVy1jTvJz7f4o99Bv1fv375GamooqVarITff29kZycjJKly6tnoIRERERkUr57sqho6PzQ/2gVvUjVVNT86sGJQICAnDw4MECX++sWbPQoEEDuaCEyNDQEDo6OgW+TWWKFy9eaOsuVaqUys++5j4Wppz2MbvPffDS0NDAvHnzsGnTJty/f/+z1vG5crqvLFq0SGHat/Rwmd9r90e7h35r9uzZg3fv3ilML1q06DdVb4iIiIjoX//55JdaWlrqLgIyMjLg5uYm5X8oKBcuXICfnx+cnJwKdL2fQ1Oz8Kqatna+G/58d/Kzj19Sp0uUKIGBAwfC1dUVqampn72egnLo0CGlQZJv5ZwX1rVLn+fFixfYvXu3uotBRERERPn0nw9MqJtMJoObmxtu3rxZoOsVBAHLly+HmZkZypQpU6Drph9bp06d8O7dO7Unlrx58ybmzp2r1jLkpLCuXfo84eHhGD169Hebe4SIiIjovyxfgYmIiAisXbsWv/76qzQ6QEpKCk6fPg0XFxepj/XJkyfRpUsXNG7cGCNGjEBcXJzCukJCQuDi4gIbGxu0bNkSxsbGMDY2hrm5Obp164bt27cDAIKCgjB9+nQMHjwYqampcHV1hampKby8vKR1BQYGYvLkyejTpw/Mzc3h4OCAM2fOKGwzOTkZK1asgK2tLbp27YoBAwYgICBAYb6YmBjs2LEDtra2Sh/Orl+/DmdnZ3Tv3h1mZmYYPnw4nj9/DiAzIPD3339j3rx5aNmyJW7duoXnz59jyJAhaNy4Mbp06YJnz55J61q+fLn0YHPhwgU4ODjAwcEBGRkZADJH0ujVqxfs7e3RsGFDGBsbY8aMGbmeq5s3b+Lly5dKcy/IZDJcvHgRzs7OGDRokDT99evXWLduHTp37oy1a9ciKioK06dPh5mZGaysrODr66t0W15eXujduzccHBzQtm1bzJo1C1FRUTmW79KlS2jVqpV03sX6FB4eDmtra2m6suN/7tw59OrVC507d0aXLl2UNqMv6H308PCAvb09OnXqhHr16knl69y5M3r27Km0jmcVGBgIFxcX9O7dG9bW1ujQoQPWrl0rneeswsLCMGnSJNjY2MDe3h5Tp05Vuf6MjAxs27YNdnZ26Nq1K3r27KmyG0Ze61L58uVRpUoV6RpU5caNG2jQoIF0LDp16gQAWLt2rdz0Fi1a4J9//gGQee5MTU1hbGyM3r17A1B+X7l58yZWrFghtUQQr4uzZ88qlOPdu3cYPXo0GjduDFtbW9y5c0dhHkEQ4OHhgb59+6Jbt25o1aoVJk2ahJCQEGmetWvXSmUzNjaWpp85cwYWFhbS9Ldv3wLI/dpVpiDvoaGhoZg2bRocHBxgY2ODbt264fTp0wrzJSQkYNmyZdI107ZtWyxduhSfPn2Sm+/9+/dYsGABOnXqBEEQ8H//938wNTXFunXrAACXL19G79694e3tjaioKIwbNw4mJiZo166dynwfN27cwNChQ9G7d29YWVlh8ODBCiMdiaMjOTg4wNvbG8+ePcNvv/0GGxsbhIWFAcj9+gkPD8eMGTOkhKKzZ8+Gg4MDVq1aJS2/aNEiNG/eXDp/CQkJOHLkCJydnWFjYwMg8zq3sbGBiYkJpk6dqrTV0LNnzzBixAj89ttvMDc3l+pFy5Yt0a1bNxw7dkzpsSAiIiIi1fIcmHjz5g127NiBLVu2SD8WgcwffHp6evDz80NiYiI2b96M0NBQzJ8/Hw4ODrh48aJCcsl3795JD3O+vr64fPky7O3tAQDFihXDwYMHMWTIEKxbtw7dunWDl5cX0tPTsXjxYly8eBEJCQk4dOgQgMwfiZMnT8a4ceOwb98+nDlzBvr6+hgzZgx8fHykbaakpGDIkCG4efMm9u/fDx8fHzg5OWHHjh1yZYuMjMSWLVuwefNmvHnzRuE4eHl5Yf/+/Vi6dCkOHTqEAwcO4OnTp3B0dERAQADev38PTU1NXLp0CR8+fMDFixfh7e2NCRMmYPr06Xj58iVcXV2lB65JkybBzc0NANCmTRt4e3vD29sbWlpaePz4MaZPn44VK1bg6NGjOH/+POrXr5+n8+Xn5wcAcg9Yov3792P79u24cOGC9MNeJpMhIiICL168wMuXLxEUFIQlS5bAzs4Oq1atQkZGBqZNm4bQ0FC5dc2cORMeHh5Yv349vL29MW/ePBw8eBCDBg1CWlqayvK1bt0aFy5cQIMGDeSmV6xYEefPn5cecrPbs2cPXF1dMW7cOPj6+mL79u3Yt29foe7j4sWLMWfOHEyfPh3Hjx+Hl5cXihYtCgDo378/Dh48mGOCzYiICPTp0wdFihTB/v37cfbsWZiZmWHNmjUKzc5DQkLQvXt36Onp4cSJE/Dx8UGlSpVw7tw5hfUKgoCJEyfi4MGD2LBhA3x8fDBr1ixs2bJFYd781iUjIyMEBQUpDdyJLCws4OXlJeXw2Lt3LwBgzJgxcHd3B5B5PV+4cAGNGjUCAClw1axZM+zevVvlfaV58+Y4cOCAlOdFvC7atWsnV4YnT57A3d0dQ4cOxeLFixEREYHx48cjJSVFbr5Jkybh2LFj2LBhA7y8vLBnzx7cu3cPv//+Ox4/fiyV+9SpUyhSpIjcsu3bt8fly5dRqVIlhXWqunaVKch76LNnz+Dg4ABzc3N4e3vDz88Penp6GDt2LE6cOCHNFxMTg969eyM1NRX79u2Dr68vZs+ejZ07d8LR0RGxsbEAgAMHDsDBwQE7duxAUlIS3N3d4eXlhYSEBGzduhXOzs4YPnw47t+/j7S0NDg5OUn/HxISgj///FMhOLFv3z5MmTIFf/zxBzw8PHDixAno6Ohg8ODB8PT0BJB5vx06dChGjx6NJ0+eID4+Hi4uLggKCkJgYCCuXr2ap+unYsWK2LZtG9q0aQMAcHNzg7e3N8aPH49//vkHu3fvhru7u9woS4GBgRAEARcuXEBKSgoWLVoELS0tLF26FK1atYKPj4/C98Pjx4/h6OiIMmXK4MSJE7h8+TKaN28OAKhSpQq8vLxgZ2en9PwTERERkWp5DkzUqFEDU6dOVXgDL74pErsLNG/eHEOGDEH9+vXh6uoKbW1tXLhwQW4Z8U310KFDoaurC01NTUyaNAna2toICwuT3mKOHj1aelsXHByMatWq4dKlSxg2bBicnZ0BAFOnTsWIESPw888/AwBKliyJwYMHAwBWrFght8379+9j6dKlMDAwAJD5cJz9R6ShoSEmT56sdMjEiIgIzJs3D25ublIyx5o1a8LOzg6JiYnYsGEDKlSogCZNmqBevXrS+qZPn46GDRuiZ8+eaNiwIQICAhAUFJTrMffz84OBgQEqV64MAChbtixmzpyZ63IAcPfuXQBAtWrVFD7r27cv/vjjD7lpmpqasLCwkH5kx8fHY/78+bC0tESLFi3g4OCAtLQ0XL16VVrGx8cHnp6emDt3rnT+zc3Noa+vjxcvXuT4UAtk5gmoWbOm0s9q1KihMO3ly5dYuHAhnJycpOFdDQ0NFfalIPcxJCQE7u7uaNKkibRc7dq14eDgAADSW++cnD17FpGRkTA3N4eGhgY0NTXRq1cvAJDrBiAIAiZPngx9fX3Mnj0bWlpa0NTUxPjx4xWGQgUyHyZPnjwJNzc3qf43aNAAAwcOVJg3v3VJHNHgypUrOe6bkZGR9LY5a0sgMzMzNGnSBElJSQqJCO/du4dRo0ZBR0dH5X0lr9LT0zFz5kyYmJigQ4cO6NChAyIjI+VajXh5eeHYsWOYOXOmFECqVq0a5s+fj/j4eEycOFEKopUtW1ZpYkodHR3pGH+ugrqHpqenY8KECWjSpAm6du0KILNut27dGgBw/Phxad4FCxYgOjoakyZNkgImrVu3hpOTE168eIH/+7//AwD06tVLCiwkJCQgLi4ON27cwIQJE/Dnn39i48aNUp0/ePAgxo4diytXruD69euwsLAAACxbtkzqRhEQEIAFCxZg2LBhqFWrFoDMBLiLFi1CiRIlMHfuXLx+/RqGhobYtm2bNLStj48PtmzZAg8PDzg6OqJt27Z5vn5UadSoEWbNmgUjIyO56fXr14etrS0AIDY2Fn369EGPHj3QoEEDTJgwAQBw/vx5uWUWLVqEpKQkjBw5EhoaGihSpAhcXV0BAA8ePPgm8rIQERERfY/ynWNC1egKurq6AICGDRtK0/T19WFgYKDQrP/BgwcAgAoVKkjTKlSoID00ZX2rJb6l1NPTQ79+/VC8eHFMmjQJrVu3xsOHD/HixQusXbsWPXv2lP6tXr0aFSpUgCAIiI6ORlxcHHbv3o2GDRsqPFyoGuJP2X76+voiLS0NI0aMkNve5cuXUaFCBbx//17heGRvESA+YEZGRirdblYymQz+/v7YuHEjZDKZVF7xOKmSnp4utfZQlYU+t/NYr149uYSVysq9Y8cOVKhQAXXr1pWmaWpqwt3dHQsXLlTaWiM7DQ2NXOcRbdq0CWlpaQqtKZo0aaJ0/oLYx0ePHkEmk6FixYpy62jcuDGAzDfSualbty5q1Kghd22ID6FZ+8Nfu3YN9+/fh42NjVxyRw0NDWl7ooyMDGzcuBHlypWDubm53GfK6nR+65LYUkFZq6HsxAfWI0eOyE1v1qwZAEhvx4HM7lQPHz6Ugjyizx21RWyJIRLvKVnPobu7O0qXLo3atWvLzWthYYFffvkFQUFBcg+g+amTn+NL76EXL15EYGCg1DpA1LdvX/z111+YOHEiAODDhw/w9fVFkyZNFEam6dOnDzQ0NODr64uPHz8C+PdeKwgCnJ2doauri5EjR0rBD/EasLe3h7W1NYDMQPCyZctQpEgRKZgBZLZsSk1NVaibBgYG6NixI9LS0qQWNlnX/dtvv+Hnn39Gw4YNMWfOHJQpUybP109ulB138ZgbGBjIfTeIgbns311il6Ss94MGDRpAR0cHgiDk6X5ARERERIryndpeVTNlVaMu6OrqIj09XW6aOPRhVFQUfvnlF2l6mTJlEBwcjHLlyimst2zZsgoPDI8ePQKQ+aZO1Zt3ILO/dlJSktK38OIP0+yU7eejR49QqlSpPA0NmNPxAKBwTJTp3r07PDw8sGLFChw/fhwjR46Era0tRo8eneNycXFxUvcFfX39fJUvr+VOSkrC8+fP5R4WRI0aNVJ4YPxSYs4ITU1NVK9eXWnZsvvSfQQgvWHP/oAiPhhlrauqmJiY4OTJk9J6vLy8cPnyZQCQG81BfDjOSz19+vQpwsLCpIf/nOYF8l+X9PT0AOQtgGZhYYHKlSvj1KlTmD17NvT09JCeno5r166hRIkS8PHxgYuLC3R0dHDmzBm0a9dO4VouqNFxxAdw8c11WFgY/P39FeqMyMzMDK9fv5YCQl/Dl95DxdYgWQO7QGa3mZ49e0p/X7t2DRkZGVILsazKly+PatWqITAwEA8fPoS1tbVUrhIlSkhdlbISz1mJEiXkphsaGsLS0hIXLlxAYGAggH9b2ijbtpmZGTw8PORatYjbLlu2rML8eb1+cqPsuOf3Pl2yZEl8+PABUVFRUnBCQ0MDpUuXRkxMDIcjJSIiIvpMahmVo0uXLgD+7W4AZP7ADA0NRe3ataW3VbkRE8IpG7M+q+DgYABQ6DueX3FxcYiKivpqWd9r1qwJT09PNG3aFC9fvoSLiwt69uwp/fhXJS9Bjy8VFxcHQRCkt62FLSoqCgkJCdDR0SnUoUeza9asGSpVqoSnT5/KnXcxD0X2nAeqxMbGYu7cuRgzZgxq1aolNaHPSqynyh4KsxO7O+W1Tue3LokPZnmp65qamujatSsSExOl5It+fn6wtrZGp06dEBkZKXVF8PHxkVpYFCbxgTU8PBwApKSI2YkP90lJSYVepoIi5oXILWgUEREBACoTp4r7npyc/MVlEoNpWZNRAsqPu/hAn59jnpfr52sQW4/8/fff0rSkpCRERUXByspKZZCUKKvHodFYfOoxvO8r79IZm5SKzVdeYtT+Wxi44yqm+9zD3eCcr/f1l16gz7Yrcv/WX3ohN09qegb2336DsR630d/9KsYfuI39t98gOU15wt7E1HRsv/YKM4/cx+yjD7Du4nPEJLK7EhERFQ61BCYcHR3h5OSEnTt34uXLlwAyE+fFxsZizpw5eV6P+FZXfHuWXUBAAFJTU6UHWfEH/efS09ODTCaTy0GQlTgyR0GqVasW9uzZg82bN6NWrVp49OgRBg0alOOPegMDA+ntZkE8dCgjtsQIDw+XHoCyyy3HRH6I5zAlJeWrPkTq6upi48aNKF26NJYtW4aMjAx8/PgRu3btQtu2bfMUmAgJCYG9vT2Sk5Oxe/dutGnTRml3AXEf89IcXFw+P3U6P3VJTB6Z1y4WDg4O0NDQkBLO7t+/H3369EH37t0BZHbnCA0NhaamZq5dkQqSWP6oqCiFhJjAvwGYr1mmLyXuk9glLjvxuhPnE4ME2YmtS7In9fwcYn0UuwCJ286a5DP7dvN6zPN6/XwN48aNg729PdasWYOwsDDIZDKsXr0aJUqUwNSpU9VSJvp+fIhPhs+DYGy7+goP3kZDpqTBz6eUdGy96o8GPxlgcvt66NPsF0TEJWP52ad4GaE8yBgWm4gHIVGoVKqY3L9O9eVzE62+8BzHHr2FTBCgramBDwkp8H30FvNOPERqukxu3oSUNPx1/CESU9Ph1rkx3Owbw0CvCP70fcDgBBERFQq1BCaAzMSH1atXx7Rp09C1a1fcvHkT+/btU5nzQRmxz7inpydev36t8PnOnTuhq6srdRcRu358LnF7q1evVnjg//Tpk0If+y+1YsUKqUl669atcfjwYbRq1QphYWG4du2ayuV0dHSkJsWq3hR/qeLFi6Nq1arIyMhQGFkCyByaUtlDSXbiQ0r2t7rim1fxvwYGBlKzcHEUha+laNGi0NXVRWhoKLp27QpnZ2fY29tj5cqVeVp+/vz5CA8Px+TJk3PsspCfeirO6+/vn6fgU37rkthSQlnSTWV+/vlnNG3aFLdu3YK3tzfq1KmD0qVLo0GDBqhduzauXr2KDRs2SEkOv5aaNWvCwMAAgiAoTeQp5rMRE0cC/9bJ7EGfrKO7qFOdOnUAAEePHpXLxwNklk3M6WFmZgYgsz4pG743JiYGBgYGCnlwcqNs/9+9ewcNDQ00bdoUAKT/Xrp0Sel2AcDKyipP28vr9fM1iNv/6aefMHLkSHTr1g0fP37EwYMHlSYaJsqqXImi6Nq4KhybKnbXEz18F40RVkaw+KUcapQtDpu6ldHP/BcIAnDzzQely3jdC8ao1sZY1t1M7l81w38Dy3cCPyI5LQPLephhnaM5tg2wxBDLWtDUAF5/TMCFl/IBzH233+BtTCIGWNSEpmZmINDBpCo+paZj27VXBXA0iIiI5OU7MKHqx7mY1V7Vj/as0w8dOoT9+/dj586d8PLygo+PD9auXat0+EKxSbay7glmZmaoVq0akpOT4eTkJGVoT0pKwpIlS6T+yi1atIChoSHevn2r9Idy1vLntJ9dunSBtrY2/P39MXz4cKnpfUREBMaNGyf3Qzs/x0N8EMq+j/Hx8XJD/+nq6sLR0VFuGVXEAI+qbi7i9rOXTyxDXsrdrVs3AJnJBY8cOSKdq6tXr2LNmjVyCQ6zBxpE4jk6fvw4MjIykJGRAU9PT2m4U7H8Ghoa0ggqWZPmZZU9I35B7GNqaiqGDx+Orl27YsOGDfD19cWhQ4cwYsSIPDfbFutJ1jomllXcliAI6Ny5MwDg9OnTKrvIiOswNjaGsbExkpKS4O3tneO8QP7rkhhUyprYNDcODg6QyWRwc3PDoEGDpOndu3eHTCbD6dOnVbYwUXVfUXVt5BYkEKdrampKo5RkH84SyMzX0KJFC7nEmGKdPHr0KIDMUSoWLVokjaST9ZpSVb6cfOk9tF27dihdujQSExMxbtw4fPiQ+bASFxeHadOmSfl2jI2NYWlpifT0dGl4ZVFCQgL8/f0xcOBAKdFqTvfarLLXzU+fPuHWrVto166dFMgSj7mvry8+ffokN/+9e/dgaGgodYvIuu3s9wcg79cP8O/5ULYecVrWvBT5uU8DmaM7PX36FNu3b4ePjw8OHz6MJUuWMChB+aJfRHV6L4tfyqF4Efl78v/KZ+Z1KVdcsZvf2+hPePUhHj+XUZ5PSvQmMgGTO9RDxZLFAACaGhpoV6cS2tepLK1HFJmQgksvI/C/ciVQsui/ZSmqowXjCqVwNzhSbn4iIqKCkK/AREZGBl69yoyUZ22hICYDAyB1zQAyfyiLb8eyTr99+zbi4uLQsmVL2NjY4LfffoOdnR26desGV1dXPHz4UJpXbJbs7+8vN+oFkPn2auHChShWrBhCQ0MxcOBAmJiYwNTUFJcvX5aGFC1SpAj++usvaGlpYdq0adIb97i4OBw7dgxAZrPot2/fSj94X7x4obCfVatWxZQpUwBkDhPZvn17mJmZwcrKCpUrV5aGzRNHQBDLnZWYGyDr9J9//hkaGhoICAiAIAg4d+6c9ON/3rx50pteQRBw6dIl1KhRQ2FUg+xatmwJACqHJRW7nWTd56zTs5dbfDjIOn3IkCEwMTFBeno6pkyZAgsLC5ibm8PZ2RkzZ86UG1lCPJ7Zu7uIwRw/Pz9YWlrCwsICd+7ckYIQmzdvxuTJk5GRkYFx48ahevXq8PPzkxtdYt++fQAym+s/e/ZMelAriH2MiIhAUFAQVqxYgXbt2sHW1hYdO3aEvb09+vfvj82bN+c6RKCYCHTWrFl4+PAhPD09MW/ePACZ9eHChQu4evUq6tWrhyFDhiAhIQFjxoyRcggEBARIw5LeuXNHqkPz5s1DsWLFsHjxYqmOpKSkwMvLCwDw5MkTvHv3DgkJCdL8ea1Lb968gba2tlSP8sLGxgZ6enpo3bq13AgH9vb2KFKkCOzs7JQGc1TdVwBI6/H390dMTAxOnToF4N/7SfZRQ8RzKK4PAJycnNC8eXNcuXIFW7dulR5M9+7di4iICMydO1duHWKdnDdvHqysrNCiRQv88ssvUiuVCRMmYOvWrVL5VF27yhTEPVRfXx/z58+HlpYWbt++jV9//RVt2rSBhYUF4uLi0KNHD2n5+fPno3z58tiwYYM0YkZqair+/PNPNGnSBEOHDpXmFcv14cMHhWsjqz179kjzpqenY/78+ShSpAhmzZolzWNmZgZnZ2dER0dj2rRpUnehe/fu4dChQ1iwYIGUBDnrtq9fv66wvbxeP8C/9eXly5cQBEFqPZKYmCjVjazHXbwXREZGyrUqEef9+PGj3PTbt2/j1atXct9dnTt3Ro8ePTBjxoxc8/8QAUB+eyKFxiShWhl9WNeuqPCZ1/1gvI9PxliP25js9TdOPnmH9AzFQFt3k2oooq3Y4qh+5dIA5IMefwdHQgCUBjtqlM1shXEvWLEVFhER0ZfQEPKY1vzZs2cYNWqUlPRPR0cHv//+OywsLDBnzhyp2bO+vj4GDhyIn3/+GUuWLJF+1Onr62PUqFEYOnQowsPD0b9/fwCZD5PJyclyb+l0dXVx5MgR6a2U+FZLX18fY8eOxeDBg+XK9vz5cyxfvhx37tyBlpYWrK2t8ccff0gjJ4iuX7+O5cuXw9/fH+bm5qhevTrKlCmDPXv2wMLCAi1atECzZs0wePBg6QempqYmWrRoIT2IAMCpU6ewadMm+Pv7w8DAAD169MCoUaOgpaWF4OBgDBgwQHrjrK2tjebNm2Pq1KkYOXIk3r59K01v27YtVq9eDSDzAXzjxo0wNTXFgAED0KpVK7i5uUmtAypWrAhDQ0PUqVMHEydOhKGhYY7nKyoqCtbW1ujatatC3o7Zs2fDx8dH6nNfvnx5bN68GXPnzpXLlF+tWjXs2bMHzs7OePbsmRQIqFatGg4fPgx9fX0kJiZizZo18PX1RWxsLBo3bgxXV1e54S0HDBiA27dvSw+EVapUwc6dO6Ukp7t27cKGDRv+X3t3Hl/jlfhx/JuIa4sliF3KRKNRtCGMMgSx1c/eENTWWqK1K+0YRhUtRqu1VKuTVkMpP3vbadLaS62/Ymas0aCWBpFIJSKS3Dy/P0yeupNF0pIny+f9enm95Dwn5557T55wv/csMgxDgYGBGjt2rJYuXapDhw5p/Pjxaty4sdlWdHS0/va3v2n79u2qUqWKGjRooM6dO+vll19Wo0aN1KJFC3Xo0MH82XkYz/H999/XmjVrlJqaqri4OCUlJTl86tqrVy/NmTMn07GIjo7WlClTdPjwYVWrVk19+/ZVYGCgevfuraioKI0aNUrPP/+8WX/lypVasWKFbt26pWeeeUbe3t46c+aMTp06pRYtWqhly5bm0oMTJ05o3rx5OnbsmHx8fFS7dm15e3vrnXfeUbNmzdS8eXN16tRJixYtyvbPkt1uV7NmzdSkSRMtXbo00+eVkalTpyowMDDdaS2vvPKKhg4dmm4GRma/V2bNmiXp3ukj48ePl91uV58+fTRgwAB17drVYSZNrVq1tGLFCg0aNMgMKpycnNS0aVOtWLFC0r034x999JG2bNmi1NRUubu7q379+goKCkp3skpSUpJmzJih0NBQVaxYUePGjVOXLl00ZMgQVa1aVaNGjXLYoDejezcjD/N3qCQdOHBA7733nk6cOKFy5cqpV69eGjVqVLrw5/r163rvvfe0e/duVahQQSVLllS7du00ePBgc4bB4sWLFRwcbC4LKlasmPr3768///nPZjuLFy/WkiVLNGzYMJ08eVIxMTG6ffu2GjVqpFdeeSXdKSHSvVknISEhunbtmmrUqKFKlSpp5MiR5s9BdHS0evfu7TALpVq1agoJCZGHh4dZJ7v3z61btzR69GidPHlS3bt310svvaQTJ05o6tSpZmBZvHhxDR8+XK6urlq4cKG5bKlcuXKaMGGCYmNjFRwcbC6DK1eunKZNm6auXbvq7NmzeuGFF2Sz2RQbG6vExESH2Rlubm4KDQ3N8DSSrPj7+997jQf56c61izn6XuQ/JyNjNfvrf6uXj4cCGmU92yYqLlGLd57WhHb15FbS8d6+m2LXigPnFJeYrIioON38z94PtSq4alL7eipf6sGbI+/98br+vjdc7wT4quJ/womP9oRrV/g19WtSS10bOh6xHnr8ilYePKfmf3DX6DZPZNQk8rkSlT1Ub/B0q7sB5Atp/35v377d4p4UDNkOJh6m9evX66efftIrr7ziUJ6UlKTLly/r9ddfV9u2bdMFEMiZuXPnatu2bdq2bZvVXcm34uLiFBQUpIULFzq8gU0LKXbu3KlZs2Y5nDCT3x05ckT9+vXTunXrMjwOFoVTWjAxZ86cXDlZJS9auHChKlWqZC6DSpOYmKjz58/rlVde0fjx49WhQ4cctUswUbhkJ5i4fPO2dpy5qt3h13Qn2a7KpYtrUocnVb1cyUzb/eflGK08cE4//3JHNd1K6s0ePnJ5wClW720/qVoVXNXjaQ+zbP63J3T0UoxebF5H7bwdN8fdFX5VH+05q4bV3fTnTumX3yJ/uJOUnOm14pVqyvv5KZlez+wYeqAwIph4uDJf6PiInD59WrNmzcpwI7q0jSr9/f05D/4hCAoKUmhoqL7//nu1aNHC6u7kS3/961/VsGHDdJ+qOzs7q2zZsurRo4dCQkIs6t2jsXbtWj333HOEEsB9du/erbVr12Z4KlPx4sXl7e2tFi1aZDpbIu0/LxmJjIx8KKejoOCoVq6kuj9VUx7lS2nLsUu6Fpeov317Qu8ENM40bHiqRnl5di2tt0KP60J0vPZFRKnV4+lnMqX5MSpOCUl2dWnoeER72lKQIs7p15zY/3OUiEsRa07GwcPR8q8rsq4Q9Gamlyz4PBNAIZHrp3KsXLlSdrs9093VDcNQeHh4jj9xQnpubm5atGiR5s+fn+FRicja1atXFRoaqmLFMp8Oe/DgQYc1/fnd6dOndfr0aU2ZkvmnJSic8sqpJFZZvnx5lpsO3717V/Hx8Q5Lz4DfytnJSWVL2NTaq4re7OGjSqWLKyouUWeuZnxkaBrXYkU1ouXjcpIUEZX5qVy376boq39d1nh/73RBR+kS937O4++m/1T9TvK93wP3b4oJAMDDkOszJtq2bavNmzdr5MiRmjx5ssOnsrGxsVq3bp2GDRumkiUzn66I7Hvqqac0fvx4zZgxQ7Nnz7b8uL38xN3dXT4+Plq+fLnKlSunwMBAh5/L/fv369y5cw77Q+RnUVFReu+997Rs2TKVLl3a6u4gj0nbNPL+zTkLk/bt22vmzJkaN26cxo8fb55+It3bJHfDhg2aOHGinDP5NDuraZ5ZzaYAStpc1LFeNa08eE6/3Ml6s2Xp3h4TlcoUV9EiGf8sJqXYte7IBb3Q3FMlben/G/hY+VLaFxGlW4npg4lf/rOPhccDTgFB3rZn1qBMrz1oKQcAPCq5Hkz4+/trw4YNWr58ucaNG6eEhATVrl1bHh4e8vb2Vv/+/Vm/9pC1bt1a1apV09KlSzV69Gg55XRL8EKqSJEiWrFihdauXauvvvpKS5cuVbly5fSHP/xBNWvWVJcuXQpMKHH27FmFhYXp7bfflqurq9XdQR5y9OhRjR492jxtJCQkRLt379bq1asfuAlvQfL888+rbt26WrFihQYPHizDMFS7dm3VrFlTPj4+GjZsWLaPEAZyqkrZe8d8ZrXHxP2KOjvryf+cuHG/e6HET+rl81imsx4ae1TQ54cvKOJ6+hkXl24mmHWQf5WwZT7jpUQxG/8PB2CJXA8mJOmJJ57QvHnzrHjoQsvLy0t16tQhlMghm82mgQMHmqfIFFSenp4aM2aM1d1AHuTj46Pvv//e6m7kCb6+vvL19bW6G8jnfssS/Qs34lWvalk9VuHBwfGV2ARVdyspn5qOJ5PF303WlmOX1P3pmnIt9usbU8MwtP9clFycndW0dkVVK1dSTWtV0A8/xSj+brJZNyEpRWeu/aLmnu5yL11cAAA8TJYEE7BGZlOMAX42ACB3RN++t+dTdHz6vZ82H7uoX+4kq0O9qqpa9t7siBM/x+ropRhN8Pd2qLs7/JoOXbihLg2qy7tqOUn3TvP4+vgVDf/T4w51o/6zeebdFLuOXIoxyw3jXmBxN8WuD/o3M8tfaF5HP0Yd1fojP2nIM3WUmmpo9aHzKl+qmAb+8Q8P5XUAAOB+BBMAAACPWMztu3pv+yn9FHNbkrT77DWdj47X801rq0H1e6e53Emya/+5KG0/Hak67qVVpWwJ1XQrpWmdG8jm4rhHlFtJm67HJWpO2HHVquAqT/fSql3RVUNbPO5wosbNhCT99YtjGe4ZkaZJrQoO+02ULWHTzK5P67OD5zRty1E5OzmpdkVXvd7lKTa+BAA8Ek4G5/4AAGCZtM0vFw/y051rFy3uDYDCrERlD9UbPN3qbgD5Qtq/31ltcI3sY/42ABRgJ06c0IQJEzRkyBCru5KlpUuXqnHjxtq/f7/VXQEAAEAuYykHABRQn3/+uT777DP9+OOPatq0qdXdydL169cVHx+vmzdvWt0VAAAA5DJmTABAAdWvXz/NmTPH6m5ky/Tp07Vnzx517tz5d7d19OjRh9AjAAAA5BaCCQAowMqWLWt1F7LF2dlZlSpV+t3tRERE6H//938fQo8AAACQWwgmAKAAK1KkyIMrFRB2u10zZ84UezoDAADkLwQTAIB8LzU1VTNnztSBAwes7goAAAByiGACAAqQ9evXq0ePHurevbt69uypHTt2ZFhv3759GjlypAICAuTr66sRI0bo9OnTDnUiIiI0bNgwPffcc2rSpInq1q2r9u3bp2vrhx9+0PDhw9WrVy+1bdtWw4cPd2grJSVFmzdvVseOHXXw4EHt27dPfn5+CggIUHx8vGJjY/Xpp5+qU6dO2rhxo/l9t2/f1kcffaRmzZpJkvbu3auuXbvqqaee0qBBg3T27Fmz7oIFC8xQYufOnerVq5d69eolu91u1tm/f7+GDRumvn37qlWrVnrhhRfSnQJiGIa2bdumXr16aePGjTp16pSeffZZdezYUZGRkZKk3bt3KzAwUN26dVPDhg1Vt25dTZ06NVvjAwAAgPQ4lQMACoh58+Zpy5YtCg4OVr169XTx4kUNGjQoXb0NGzZo165devvtt+Xq6qqIiAgNHjxY/fr10/r16+Xp6an4+HgNGzZMr7/+ulq3bq2kpCS98cYbOnTokENbmzZt0vz5883HvHnzpjp06KDnn39eGzZskN1u19SpU80NKa9cuaL33ntP165d09WrV/Xdd9/pxIkT2rRpk6Kjo812d+3apTlz5ujChQuSpCNHjmjMmDFydXVVYmKiDh48qAEDBuiLL75Q5cqVNWnSJLVs2VKDBg1SmzZtNHfuXId+rl69Wh988IGWL1+uOnXqKD4+XhMnTtQLL7ygWbNmqXfv3oqOjtarr76qvXv3SpLi4uI0YcIEXbx4UXa7XXv37pW3t7f+8pe/aN26dapWrZpu3LihoKCghzmMAAAAhQ4zJgCgANizZ48++eQTTZo0SfXq1ZMkeXh4aMyYMQ71rl27ptmzZ2vmzJlydXWVJHl6eqpLly5KSEjQBx98IOneLIiff/5ZDRs2lCTZbDZNmzbNYTPNCxcuaPr06RoxYoT5mG5ubvLx8VF8fLx2794tT09PrVmzxjyudPPmzWZ4MmjQIPn7+2vy5MnpTuNo3bq1Nm/erJIlS0q6NxMkLCxMe/bs0apVq1SmTBnFxsZq8eLFD3xtIiIiNGfOHA0fPlx16tSRJLm6umrevHkqXbq03njjDZ07d04VKlTQxx9/rJ49e5p9/fvf/641a9aoX79+8vf3V2hoqNzc3FStWjVJUsWKFTVt2rTsDhMAAAAyQDABAAXA+++/ryJFiqhTp04O5T4+Pg5ff/nll0pOTlZQUJD69Olj/vnuu+9UuXJlXb9+XZLMDSSnT5+uuLg4SVKJEiXUunVrs63Vq1crKSlJbdq0cXiMGTNmaMaMGerRo4dZVqVKFUlSYGCg3Nzc1LJlS02dOlXFihWTJDMkuV+JEiVUrlw5SdKUKVNUuXJlSZKvr6/Gjh0rSdq6desDN7v87LPPlJSUpD/+8Y8O5W5uburcubOSk5O1atWqdH199tlnVbNmTTVs2FAzZsxQ+fLllZqaqrNnz+rDDz9UamqqpHuvcVpQAQAAgJxjKQcA5HMxMTE6evSoqlevbs4wSGOz2Ry+/ve//62yZcs+8EjN5s2bq06dOtq6dat++OEHDR06VP369dPo0aPNOmnLM9LeyKepVq2a+vXr51CWdjpIxYoVM3y8zE4PcXJykiSVLl3aobxLly6aPXu2YmNjdfPmTZUvXz7T57Jnzx5J94KI/+br66s1a9aYz+VBfQ0ICNCaNWv07rvv6h//+IdeeuklderUSaNGjcr08QEAAJA1ZkwAQD536dIlSTJnH2Tl1q1biomJUUJCQpb1bDabVq9erYCAAMXGxmr+/Pnq2LGjdu/ebdb55ZdfJEk3btz4Hb3/bdzc3MzZFGkzFzJz9epVSTJnftwvLVS5c+dOth7X09NT69atU5MmTRQeHq4JEyaoT58+5l4YAAAAyDmCCQDI59JmFaQFBVkpWbKkUlNTzQ0e/9v9p2mULVtWb775pr788ku1bt1aUVFRevnll3XixAlJvy6/+Oc//5lhWxERETl6Hjnl7Owsm81mBhSZSetn2qka9ytatKgk5WgpRp06dfTZZ5/po48+Up06dfTvf/9bQ4YMyXa4AQAAAEcEEwCQz9WqVUvOzs6Kjo7Wzz//nGXdJ554QpK0aNEiJSYmOly7ffu2tmzZIkkKCwvTsWPHJN17I75s2TKNGTNGKSkpZh1vb29JUkhISLrHuXHjhrZv3/67ntf9/ntWxJ07dxQTEyMfHx+5uGS9KrFJkyaS5DDbI01sbKwkqVWrVtnqx7vvvqukpCRJkp+fnzZt2qSWLVsqMjJS33//fbbaAAAAgCOCCQDI58qUKWNuSnn/Jo73S05OliR1795dLi4uOnv2rEaMGKGLFy9Kundax9ixYx3eoK9du9ahjSFDhkiSGQT06tVLknTs2DG9+eabunv3riTp/PnzGj16tPz9/c3vTdug0m63Z9i/tPLMlmVERUU5fL1jxw5J0uDBg82ytNkPKSkpDnXT6nz55Ze6ffu2w7UjR46oQoUKDht1ZtXXuLg4ff311+bXNpvN3E8j7fEBAACQMwQTAFAATJ06VeXLl9fy5cu1adMmSffeWKeFC+fOndPFixfl6uqqV199VZJ08OBBtW/fXr6+vmrVqpWqVaumZ555xmxz48aNCg4ONsOCHTt2qHjx4mYg0bhxY7344ouSpBUrVqhZs2Zq1aqVOnXqpE6dOsnT01PSvbDh3LlzkpTprIIzZ86Y/czIvHnzzBkeFy9e1IIFC9SrVy+H8KNmzZpycnJSRESEDMPQ9u3bdePGDfn6+mrkyJG6efOmpkyZYi65OHLkiNavX685c+Y4HIP6448/SpL27duXYV9mz55tbqhpGIZ2796t2rVrq1mzZhnWBwAAQNY4lQMACoAaNWpo7dq1mjt3rmbOnKnVq1erbt26at68uUqWLCkfHx/t3LlTnTp10uDBg1WlShUtW7ZMZ8+eVcmSJTV48GC9/PLL6dqdP3++li1bJg8PD7m5uWnNmjWqU6eOef21116Th4eHQkJCdPnyZZUuXVoTJ040ZyCcOnVKw4cPN2c8BAcHa+vWrfriiy9UvHhxRUdHq3///ubmkcuXL1d4eLiCg4Md+vHMM8+ob9++stvtSk1N1YABAxxmS0iSu7u7Jk6cqA8//FAjRozQoEGDzJM1JkyYIE9PT4WEhKh9+/aqUaOGKlWqpODgYNWrV0+SFB0drd69e+vKlSuSpK+//lrHjh1TSEiIPDw8zMeJi4vTsGHDVKVKFVWoUEHe3t5atWpVtjYfBQAAQHpOxoMOgAcAwCJt27bVlStXzBkVBVHarI/Fg/x059pFi3sDoDArUdlD9QZPt7obQL6Q9u/3w9xTqzBjKQcAAAAAALAMwQQAIM960KaYAAAAyP8IJgAAedLNmzd18+ZNSVJ4eLjFvQEAAMCjQjABAMhz5s+fLz8/P/MI0r59+2r6dNY9AwAAFEScygEAyHMmT56syZMnW90NAAAA5AJmTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMsQTAAAAAAAAMu4WN0BAAAglahQ1eouACjk+D0EwCoEEwAA5AG1uwy3ugsAICM1VU7OTKoGkLv4rQMAAABAkgglAFiC3zwAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyLlZ3AACAwuz69euy2+3y9/e3uisAACCbIiMjVaRIEau7UWAQTAAAYKHk5GQZhmF1N5BNkZGRkqSqVata3BNkB+OVvzBe+QdjJbm4uMhms1ndjQKDYAIAAAtVr15dkrR9+3aLe4LsSJvZwnjlD4xX/sJ45R+MFR429pgAAAAAAACWIZgAAAAAAACWIZgAAAAAAACWIZgAAAAAAACWIZgAAAAAAACWIZgAAAAAAACWcTI4PB0AAAAAAFiEGRMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyBBMAAAAAAMAyLlZ3AACAwuratWuaP3++wsPDlZqaKn9/f40aNUo2m83qrhU4QUFB2rVrl0OZj4+P1qxZ41C2ZcsWhYSEyDAMlSpVSq+++qoaNmyYYZs5Hb+ctF2YXL16VcHBwbpw4YKCg4MzrZdXxqYw37fZHavjx4/rueeeS1f+8ccf609/+pNDGWP18F2/fl1vv/22du/erTt37qhOnToaOnSo/ud//ifD+txbyBMMAACQ6y5fvmy0bdvWmDt3rmEYhpGQkGD07t3bCAoKMux2u8W9K1jOnj1reHt7p/sTFhbmUG/JkiVG8+bNjQsXLhiGYRhhYWFGgwYNjEOHDqVrM6fjl5O2C4vo6Ghj4cKFRtOmTQ0vLy9jwIABmdbNK2NTWO/bnIyVYRjG2LFj091v3bp1S1ePsXr44uPjjXbt2hlPPPGE0apVK8Pb29vw8vIyvLy8jMWLF6erz72FvIJgAgAACwwaNMho0aKFkZSUZJYdOnTI8PLyMkJCQizsWcHz2muvGTt37syyTtprv3LlSofygQMHGn5+fkZ8fLxDeU7GL6dtFxZ379417Ha7sXPnzizf7OalsSms9212x8owDOPixYtGnz59stUuY/XwzZ071xg/frwRFRVlGIZhxMXFGZMmTTK8vLyMJ554woiIiDDrcm8hL2GPCQAActnevXt14MABtW3bVkWLFjXLmzRporJly2rp0qVKSkqysIcFx7Vr13TmzBn5+fllWe+dd96RJHXo0MGhvF27doqMjNS6devMspyOX07aLkxsNpucnZ1Vq1atLOvllbEpzPdtdsdKurdcY8iQIQ+sx1g9GmnLOCpWrChJcnV11Zw5c+Tl5aXU1FTt3bvXrMu9hbyEYAIAgFwWFhYmSfL29k53rX79+rp586bDfx7x23366ae6fv26pkyZoq1btyo5OTldnWvXruno0aOqWLGiKlWq5HCtfv36kqQvvvjCLMvJ+OW07cLIxSXzLc/y0thw32Y9VpIUExOjTZs2afXq1VqyZIkuXbqUaV3G6uFLSkrSsGHDVKRIEYdyFxcXdezYUZKUkpIiiXsLeQ/BBAAAuWzfvn2SpGrVqqW7llZ27Nix3OxSgRQXF6e1a9fqxo0b2rRpk0aPHq0uXbro//7v/xzqpY1H1apV07VRvXp1SdLp06d1584dh/rZGb+ctg1HeWlsuG8fbOXKlUpMTNShQ4e0ePFidezYUbNnz87w027G6uGz2WwZvrmXpAoVKkiSHn/8cUncW8h7CCYAAMhFqampioyMlCRzqu39XF1dJUk//fRTrvarIEpMTNT06dM1adIk/elPf5Kzs7MuXLigIUOGaMeOHWa9y5cvS8p4PEqVKiVJstvtunLlSo7HLydtI728Mjbct9nTpEkTzZw5U88//7yqVq0qu92ulStXaujQoQ7hBGOV+86fPy93d3c1a9ZMEvcW8h6CCQAAclFsbKxSU1MlSSVKlEh3Pa0sPj4+V/tVELm7u6tHjx4aPny4Pv74Y61fv15eXl5KTk7W5MmTFR0dLUm6efOmJKlkyZLp2ri/LC4uLsfjl5O2kV5eGRvu2+xp3ry5AgMDNX36dH377bcaM2aMnJycdOjQIS1atMisx1jlLrvdrm3btumll14y93Dg3kJeQzABAEAuun+Pg/s3+Upjt9szvYbf58knn1RISIhq1aql+Ph4c/O1tDHJ6DVPW4+ddj2n45eTtpFeXhkb7tucs9lsGj16tKZMmSJJWrVqlTlrgrHKXZs3b1alSpXUr18/s4x7C3kNwQQAALkobVqqpAzXXaetuXVzc8u1PhUm5cuXN98o/fOf/5T065hkNB6JiYnm393c3HI8fjlpG+nllbHhvv3tBg8erAYNGighIUE//vijpJz/HmSsfrvo6Gh98sknWrBggZydf33rx72FvIZgAgCAXFSqVCm5u7tL+nW66/1iY2MlZbxpGB6O1q1by83NzfwkLu0IxKzGo2jRoqpYsWKOxy8nbSO9vDI23Le/T8+ePSX9+mk5Y5U77Ha7pk6dqlmzZqXbWJJ7C3kNwQQAALnM19dX0q8bhN0vbRPE5s2b52qfCpsaNWrI09NT0q/jkdHRhmnj0bhxYxUrVsyhfnbGL6dtw1FeGhvu29+uRo0acnFx0WOPPWaWMVaP3ltvvaWAgAA1atQo3TXuLeQ1BBMAAOSyLl26SJL+9a9/OZSnpqYqPDxc5cuX19NPP21BzwqPyMhIde3aVZLk6ekpb29vXb58WTExMQ71Tp06JUlq27atWZaT8ctp23CUl8aG+/a3u3z5slq3bq2yZcuaZYzVo/X+++/Ly8tL7dq1S3ftzp073FvIcwgmAADIZf7+/nryySe1Y8cOczmBJB04cEBxcXEaMWKEXFxcLOxhwRYaGqoOHTqoXr16Ztno0aMlSdu2bXOou3XrVrm7uysgIMAsy+n45aTtwihtR/77X8v75ZWx4b598FhlJCUlRaGhoXr11VcdyhmrR+fjjz9W0aJFFRgY6FCelJSkBQsW6OTJk5K4t5DHGAAAINedOXPGaNy4sbFs2TLDMAwjOjra6NKlizF8+HAjJSXF4t4VDGPGjDHatm1rbN++3Szbt2+f8dZbbxlJSUnp6v/lL38x2rRpY0RFRRmGYRirV682GjZsaOzbty9d3ZyOX07aLmx27dpleHl5Ga1atTISEhIyrJNXxqaw37dZjdXRo0eNJk2aGFOmTDGio6MNwzCMuLg4Y86cOcbhw4czbI+xevg+/PBDo169ekbHjh0d/rRr1854+umnDT8/PyM1NdWsz72FvMLJMAzD6nAEAIDCKDw8XPPmzdONGzfk5OSk7t27a+DAgXwy9JD84x//0KJFi3TlyhV5eXnJ19dXLVq0kJ+fX4b1U1NTFRwcrC1btshms6lKlSqaMGGCvLy8Mqyfk/HLaduFQVxcnPr376/z58+bRwa6ubkpICBAkyZNcqibl8amMN632RmrW7duadq0adq/f7+cnJz0zDPP6Mknn1RAQIDKly+faduM1cPz+eefa8aMGVnWefHFF/Xaa6+ZX3NvIa8gmAAAAAAAAJZhjwkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAAAGAZggkAAAAgj0tNTdUPP/xgdTcA4JEgmAAAAADyuG+++Ub79u2zuhsA8EgQTAAAAAB5WFRUlObNm2d1NwDgkSGYAAAAAPKomJgYBQUFKTIy0uquAMAjQzABAACAQuH48eMaPXq0OnbsqBYtWui5557TV199la5eZGSkZs6cqW7duqlly5by9/fXW2+9pdjYWLPO119/rUaNGqlu3bqqW7euFi9ebF6bPn266tevb167fPmyee2XX37RwoUL1bhxY0lSdHS0Jk2apMaNG6tdu3b65ptvzLqXL1/WqFGjzO9fuXKl2rdvr/bt2ysqKkrSveDitdde07PPPqsmTZqYj/npp58+zJcOAB4pggkAAAAUeJs3b9bQoUPVu3dvffPNN9q2bZsk6ZVXXtGSJUvMesePH1evXr1UuXJlbdy4Ud99953GjRun1atXq3v37rp06ZIkqXPnzjpw4ICaNm2a7rFmzpyp0NBQubi4OJR/9tln6tatm5YuXar4+HhFRkaqT58+OnDggOx2uy5duqSJEyfq4sWLkqQaNWro888/18CBAyVJAwcO1NatW7V161a5u7srJSVFw4cPV/ny5fXVV1/p8OHDWrJkiYoXL/5IXkMAeFQIJgAAAFCgnTp1StOmTdPQoUPl5+cnSSpRooQ6duwoSfrkk08kSYmJiZo4caI8PT0VFBQkFxcXOTk5qVu3bhoxYoSuXr2qsWPHym63S5JsNpt8fX0zfMyaNWuqYsWKDmV9+vRxmMkwf/58/e1vf9PevXv1/fff6/HHH1dKSorCwsKy9byOHTum48ePq2fPnipSpIgkqX379ho+fHj2XxwAyAMIJgAAAFCgffDBB0pOTlZAQIBDebdu3dSmTRv169dPkrRhwwb99NNPat++fbo2hgwZIpvNppMnT5qzLSTJ2Tnz/06nhQVpbDabqlevbn49bdo0c0lHqVKl9Oyzz0qSfv7552w9r+joaEnSqlWrHMp79+4tJyenbLUBAHkBwQQAAAAKLLvdrj179qhMmTIqX768w7UqVaroww8/1OTJkyXJ3G/i/vAgTZkyZeTj4yNJ2rVr12/uT9GiRc2//3d/KlWqJEm6e/duttp6+umnVaxYMa1Zs0YDBw7UsWPHJEmVK1fW4MGDf3MfASC3EUwAAACgwIqNjVVCQoK5/CIrERERkpTpbANPT09J+l0nZGQ1kyFthoVhGNlqq3LlylqwYIFKly6tQ4cOKTAwUEOHDtXZs2d/c/8AwAoEEwAAACiwUlNTJUm3b99WTExMlnXv3LkjSbp27VqG18uUKSNJcnV1fYg9/H3atWunsLAw9e/fX0WLFtXevXvVs2dPffvtt1Z3DQCyjWACAAAABZabm5uKFSsmSQoNDc2wzr/+9S+Fh4fLw8NDknT+/PkM66XNuvDy8jLL8sJeDhUrVtTrr7+u0NBQtWjRQsnJyZo6dapu375tddcAIFsIJgAAAFBgubi4qFGjRpKk5cuXKyEhweF6cnKylixZoscee0xt2rSRdC/AyGjpR9oSjrRNKiWZoccvv/ziUDcpKcncKyI5Ofl3PYfMwo+wsDDt3bvX/LpmzZr6+9//rgYNGujWrVvm0hQAyOsIJgAAAFCgvfDCC5KkS5cuacSIETp9+rSSk5N16tQpBQUFydvbW8WKFdOQIUNUpkwZRUVFpTuyMykpSQcPHlSXLl30+OOPm+VpG2Vu377dXAISERGhiRMnKikpSZJ07tw5c9+I+ze2TElJybC//x1kpIUfGW2KuXz5coc9KYoUKWIeYVq5cuUHvTQAkCcQTAAAAKBA8/Pz04svvihJOnz4sLp376769eurR48eSkpK0ssvvyzp3pKIBQsWyGaz6Y033tD+/fslSQkJCZoyZYqqVKmiadOmObTdokULubq66ueff5a/v7/atGmjvn37auTIkSpdurQkadKkSXrzzTclSUeOHDG/9+jRow5tnTx5UpJ0/PhxJSYmmuVpQcixY8dkGIa+/PJLnTlzRpK0d+9e/fnPfzb3z7h69aq2bdumwMBAggkA+YaTkd1tfwEAAIB8bOPGjfr000917tw5ubu7q0ePHnrppZdks9kc6oWHh2vJkiU6dOiQSpUqpdKlS6tz584aOHCgSpQoka7dw4cPa+bMmbp48aKeeuopTZs2TV5eXurYsaOaNGmiESNGyMPDQ1OmTNGWLVvMZSJFihRRs2bNNH36dA0YMEBRUVFmm6VKldK7774rPz8/GYahN954Q5s3b1aDBg00bNgw+fn5KSwsTOPGjZN0b7lH1apVVaZMGfXt21eBgYFyduYzSAD5A8EEAAAAAACwDDEqAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwDMEEAAAAAACwzP8DCc5g/Vl7lW0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "bar1 = sns.barplot(ax=ax,x=ing_df['counts'],\n",
    "            y=ing_df['type'])\n",
    "\n",
    "ax.errorbar(y=ing_df['type'],\n",
    "            x=ing_df['counts'],\n",
    "             xerr=ing_df['error'],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "ax.set_xlim([0,23000])\n",
    "\n",
    "# Define some hatches\n",
    "hatches = ['', '', '//']\n",
    "\n",
    "# Loop over the bars\n",
    "for i,thisbar in enumerate(bar1.patches):\n",
    "    # Set a different hatch for each bar\n",
    "    thisbar.set_hatch(hatches[i])\n",
    "\n",
    "ax.set_ylabel(ylabel='')\n",
    "\n",
    "#ax.set_xlim([0,20000])\n",
    "\n",
    "ax.set_title(\"Estimated Numbers of Unique Ingredients (including Additives)\")\n",
    "\n",
    "\n",
    "ax.bar_label(ax.containers[0], label_type='center', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7e961ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(-10, 0, '1456'), Text(-10, 0, '843')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "bar1 = sns.barplot(ax=ax,x=add_df['counts'],\n",
    "            y=add_df['type'])\n",
    "\n",
    "ax.errorbar(y=add_df['type'],\n",
    "            x=add_df['counts'],\n",
    "             xerr=add_df['error'],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "ax.set_xlim([0,23000])\n",
    "\n",
    "# Define some hatches\n",
    "hatches = ['', '', '//']\n",
    "\n",
    "# Loop over the bars\n",
    "for i,thisbar in enumerate(bar1.patches):\n",
    "    # Set a different hatch for each bar\n",
    "    thisbar.set_hatch(hatches[i])\n",
    "\n",
    "ax.set_ylabel(ylabel='')\n",
    "\n",
    "ax.set_title(\"Estimated Numbers of Unique Additives\")\n",
    "\n",
    "\n",
    "ax.bar_label(ax.containers[0], label_type='center', padding=-10, fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a0a1e29",
   "metadata": {},
   "source": [
    "### Figure S12 part 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "44174a4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "ingID = [[8,6500,'Ahuja et al.',0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7b6f108e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(-10, 0, '6500')]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "bar1 = sns.barplot(ax=ax,x=[6500],\n",
    "            y=['Ahuja et al.'])\n",
    "\n",
    "ax.errorbar(y=['Ahuja et al.'],\n",
    "            x=[6500],\n",
    "             xerr=[0],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "ax.set_xlim([0,23000])\n",
    "\n",
    "# Define some hatches\n",
    "hatches = ['//']\n",
    "\n",
    "# Loop over the bars\n",
    "for i,thisbar in enumerate(bar1.patches):\n",
    "    # Set a different hatch for each bar\n",
    "    thisbar.set_hatch(hatches[i])\n",
    "\n",
    "\n",
    "ax.set_ylabel(ylabel='')\n",
    "\n",
    "ax.set_title('Ahuja et al. Global Branded Food Products Subset Ingredient Estimation')\n",
    "\n",
    "\n",
    "ax.bar_label(ax.containers[0], label_type='center', padding=-10, fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5093b01b",
   "metadata": {},
   "source": [
    "# Final Multiplot (Figure S12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bb4ef517",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sobek\\AppData\\Local\\Temp\\ipykernel_29732\\2589326651.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[0].set_xticklabels(labels=['0','5000','10000','15000','20000'],fontsize=14)\n",
      "C:\\Users\\sobek\\AppData\\Local\\Temp\\ipykernel_29732\\2589326651.py:38: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[1].set_xticklabels(labels=['0','5000','10000','15000','20000'],fontsize=14)\n",
      "C:\\Users\\sobek\\AppData\\Local\\Temp\\ipykernel_29732\\2589326651.py:57: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[2].set_xticklabels(labels=['0','5000','10000','15000','20000'],fontsize=14)\n",
      "C:\\Users\\sobek\\AppData\\Local\\Temp\\ipykernel_29732\\2589326651.py:68: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax[3].set_xticklabels(labels=['0','5000','10000','15000','20000'], fontsize=14)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(4,1,gridspec_kw={'height_ratios': [2, 2, 1, 1]},figsize=(20, 10))\n",
    "\n",
    "plt.subplots_adjust(hspace=1)\n",
    "                    #left=0.1,\n",
    "                    #bottom=0.1, \n",
    "                    #right=0.9, \n",
    "                    #top=0.9, \n",
    "                    #wspace=0.4, \n",
    "                    #hspace=0.4)\n",
    "\n",
    "#BAR 1\n",
    "y_pos = [2.5,6.5]\n",
    "bar1 = ax[0].barh(y=y_pos, width=ing_df['counts'], height=3,\n",
    "                  align='center', color=['#F7954D', '#5688EC'])\n",
    "ax[0].set_yticks(y_pos)   \n",
    "ax[0].set_yticklabels(ing_df['type'], fontsize=12)\n",
    "ax[0].set_xticklabels(labels=['0','5000','10000','15000','20000'],fontsize=14)\n",
    "\n",
    "ax[0].errorbar(y=[2.5,6.5],\n",
    "            x=ing_df['counts'],\n",
    "             xerr=ing_df['error'],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "ax[0].set_xlim([0,23000])\n",
    "ax[0].set_ylim([0,10])\n",
    "ax[0].set_ylabel(ylabel='')\n",
    "ax[0].set_title(\"Estimated Numbers of Unique Ingredients and Additives\", fontsize=20)\n",
    "ax[0].bar_label(ax[0].containers[0], label_type='center', fontsize=14)\n",
    "\n",
    "\n",
    "#BAR 2\n",
    "y_pos = [2.5,6.5]\n",
    "bar2 = ax[1].barh(y=y_pos, width=add_df['counts'], height=3,\n",
    "                  align='center', color=['#F7954D', '#5688EC'])\n",
    "ax[1].set_yticks(y_pos)   \n",
    "ax[1].set_yticklabels(add_df['type'], fontsize=12)\n",
    "ax[1].set_xticklabels(labels=['0','5000','10000','15000','20000'],fontsize=14)\n",
    "\n",
    "ax[1].errorbar(y=y_pos,\n",
    "            x=add_df['counts'],\n",
    "             xerr=add_df['error'],\n",
    "             ls='',\n",
    "             color='black')\n",
    "\n",
    "ax[1].set_xlim([0,23000])\n",
    "ax[1].set_ylim([0,10])\n",
    "ax[1].set_ylabel(ylabel='')\n",
    "ax[1].set_title(\"Estimated Numbers of Unique Additives\", fontsize=20)\n",
    "ax[1].bar_label(ax[1].containers[0], label_type='center', padding=-5, fontsize=14)\n",
    "\n",
    "\n",
    "#BAR 3\n",
    "bar3 = ax[2].barh(y=[5], width=len(desc_count_all_df_filt),\n",
    "                  height=5, align='center',color='#F7954D')\n",
    "ax[2].get_yaxis().set_ticks([])\n",
    "ax[2].set_xticklabels(labels=['0','5000','10000','15000','20000'],fontsize=14)\n",
    "\n",
    "ax[2].set_xlim([0,23000])\n",
    "ax[2].set_ylim([0,10])\n",
    "ax[2].set_ylabel(ylabel='')\n",
    "ax[2].set_title(\"Number of Descriptors with Occurrence > 10\", fontsize=20)\n",
    "ax[2].bar_label(ax[2].containers[0], label_type='center',padding=0, fontsize=14)\n",
    "\n",
    "\n",
    "#BAR 4\n",
    "bar4 = ax[3].barh(y=[5], width=6500, height=5, align='center', color=\"#8FCD52\")\n",
    "ax[3].set_xticklabels(labels=['0','5000','10000','15000','20000'], fontsize=14)\n",
    "ax[3].get_yaxis().set_ticks([])\n",
    "ax[3].set_xlim([0,23000])\n",
    "ax[3].set_ylim([0,10])\n",
    "ax[3].set_ylabel(ylabel='')\n",
    "ax[3].set_title('Ahuja et al. Global Branded Food Products Subset Ingredient Estimation', fontsize=20)\n",
    "ax[3].bar_label(ax[3].containers[0], label_type='center', fontsize=14)\n",
    "\n",
    "# Display the figure with all subplots\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figure_S12.pdf\", format=\"pdf\", bbox_inches=\"tight\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0f44164",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
